data/Eclat.json

{
  "title": "Eclat Algorithm Mastery: 100 MCQs",
  "description": "A comprehensive collection of 100 multiple-choice questions covering every aspect of the Eclat algorithm—from the basic ideas of vertical data representation to advanced transaction-intersection logic, optimizations, and practical comparisons with Apriori and FP-Growth.",
  "questions": [
    {
      "id": 1,
      "questionText": "What is the primary goal of the Eclat algorithm?",
      "options": [
        "To compress datasets using PCA.",
        "To classify data into clusters.",
        "To predict continuous numerical outcomes.",
        "To find frequent itemsets using a vertical data format."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat (Equivalence Class Clustering and bottom-up Lattice Traversal) aims to discover frequent itemsets by representing data vertically and intersecting transaction ID sets."
    },
    {
      "id": 2,
      "questionText": "Which data representation does the Eclat algorithm primarily use?",
      "options": [
        "Matrix factorization",
        "Horizontal transaction list",
        "Vertical transaction ID list",
        "Tree-based structure"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Unlike Apriori, which uses a horizontal layout, Eclat uses a vertical data format where each item is linked to the list of transaction IDs (tidsets) containing it."
    },
    {
      "id": 3,
      "questionText": "What does each itemset in Eclat associate with?",
      "options": [
        "A list of transaction IDs containing that itemset.",
        "The number of customers who ignored it.",
        "Its price in the market.",
        "The frequency histogram of its items."
      ],
      "correctAnswerIndex": 0,
      "explanation": "In Eclat, every itemset maintains a tidset — the set of transaction IDs in which the itemset occurs."
    },
    {
      "id": 4,
      "questionText": "How does Eclat compute the support of an itemset?",
      "options": [
        "By comparing items lexicographically.",
        "By counting transactions directly in horizontal form.",
        "By summing item weights.",
        "By taking the intersection of tidsets."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat calculates support by intersecting tidsets of items in the itemset; the length of the resulting set equals the support count."
    },
    {
      "id": 5,
      "questionText": "Eclat is an abbreviation for:",
      "options": [
        "Evaluation of Clustered Association Trees",
        "Efficient Clustering Algorithm for Transactions",
        "Enhanced Classification Algorithm Technique",
        "Equivalence Class Clustering and bottom-up Lattice Traversal"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat stands for 'Equivalence Class Clustering and bottom-up Lattice Traversal', describing its structural and traversal approach."
    },
    {
      "id": 6,
      "questionText": "Which operation lies at the core of Eclat’s efficiency?",
      "options": [
        "Hashing of item pairs",
        "Random sampling",
        "Tidset intersection",
        "Matrix multiplication"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Tidset intersection allows Eclat to quickly compute supports without repeatedly scanning the database."
    },
    {
      "id": 7,
      "questionText": "Compared with Apriori, Eclat requires:",
      "options": [
        "Fewer database scans",
        "Continuous database updates",
        "No data preprocessing",
        "More database scans"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Eclat typically needs only one full scan to create the vertical representation; afterward, intersections occur in memory."
    },
    {
      "id": 8,
      "questionText": "What type of search strategy does Eclat use?",
      "options": [
        "Depth-first search",
        "Randomized search",
        "Heuristic-based search",
        "Breadth-first search"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Eclat employs a depth-first search strategy through the itemset lattice, combining and extending frequent sets recursively."
    },
    {
      "id": 9,
      "questionText": "In Eclat, what is an 'equivalence class'?",
      "options": [
        "A class of transactions with identical sizes.",
        "A group of itemsets sharing the same prefix.",
        "A set of unrelated transactions.",
        "A type of confidence metric."
      ],
      "correctAnswerIndex": 1,
      "explanation": "An equivalence class groups itemsets with a common prefix so that Eclat can explore and extend them systematically."
    },
    {
      "id": 10,
      "questionText": "Why is Eclat considered more memory-intensive than Apriori?",
      "options": [
        "It uses dynamic hashing tables.",
        "It keeps transaction IDs in memory for all items.",
        "It repeatedly scans the disk.",
        "It compresses transactions into trees."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Eclat must store tidsets for every itemset, which can consume significant memory, especially in large datasets."
    },
    {
      "id": 11,
      "questionText": "Which of the following best describes the main advantage of the Eclat algorithm?",
      "options": [
        "It supports continuous attributes natively.",
        "It generates association rules directly.",
        "It avoids all pruning steps.",
        "It works well on sparse datasets due to vertical representation."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat’s vertical format and intersection-based support counting are particularly efficient for sparse transactional data."
    },
    {
      "id": 12,
      "questionText": "What does a 'tidset' contain?",
      "options": [
        "Transaction IDs where an itemset appears.",
        "Confidence values for all rules.",
        "Indices of frequent patterns.",
        "Item prices and quantities."
      ],
      "correctAnswerIndex": 0,
      "explanation": "A tidset stores transaction IDs corresponding to all transactions containing the given itemset."
    },
    {
      "id": 13,
      "questionText": "How does Eclat extend smaller frequent itemsets to larger ones?",
      "options": [
        "By scanning the database repeatedly.",
        "By intersecting tidsets of itemsets with a shared prefix.",
        "By randomly selecting transactions.",
        "By merging infrequent sets first."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Eclat generates larger frequent itemsets by combining smaller ones that share a prefix and intersecting their tidsets."
    },
    {
      "id": 14,
      "questionText": "Eclat’s support counting is faster because:",
      "options": [
        "It relies on data compression.",
        "It clusters transactions into blocks.",
        "It uses tidset intersections instead of database scans.",
        "It precomputes rule confidences."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Support counting becomes a quick set-intersection operation rather than a costly database traversal."
    },
    {
      "id": 15,
      "questionText": "What happens if two items have disjoint tidsets in Eclat?",
      "options": [
        "Their intersection is empty, giving zero support for their combination.",
        "Their confidence becomes one.",
        "Their support values are added together.",
        "Their union becomes frequent automatically."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Disjoint tidsets mean no transaction contains both items; their combined itemset has support = 0."
    },
    {
      "id": 16,
      "questionText": "Which of these structures stores Eclat’s intermediate results?",
      "options": [
        "Adjacency matrix",
        "Tidset dictionary",
        "Prefix tree (Trie)",
        "Hash-tree"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Tidsets are often stored in dictionaries or maps keyed by itemsets to facilitate efficient intersections."
    },
    {
      "id": 17,
      "questionText": "How is pruning achieved in Eclat?",
      "options": [
        "By using random sampling.",
        "By removing longest itemsets first.",
        "By discarding itemsets with tidset length below the minimum support threshold.",
        "By sorting items alphabetically."
      ],
      "correctAnswerIndex": 2,
      "explanation": "If a tidset’s length (support count) falls below the minimum threshold, its supersets are pruned immediately."
    },
    {
      "id": 18,
      "questionText": "Which statement about Eclat’s traversal is true?",
      "options": [
        "It expands each prefix recursively before moving to the next.",
        "It requires two database scans per level.",
        "It explores itemsets breadth-wise like Apriori.",
        "It never uses recursion."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Eclat uses depth-first recursion to explore extensions of each prefix before backtracking."
    },
    {
      "id": 19,
      "questionText": "What is the output of the Eclat algorithm?",
      "options": [
        "A set of decision trees.",
        "A regression line.",
        "A list of frequent itemsets with their support counts.",
        "A set of clusters."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Like Apriori, Eclat outputs all frequent itemsets meeting the support threshold, ready for rule generation."
    },
    {
      "id": 20,
      "questionText": "Eclat typically performs best when:",
      "options": [
        "The dataset is sparse and fits in memory.",
        "The transaction count changes frequently.",
        "There are continuous variables only.",
        "The dataset is dense with many co-occurring items."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Eclat is ideal for sparse, moderate-sized datasets that can hold vertical tidsets in memory."
    },
    {
      "id": 21,
      "questionText": "The vertical data format of Eclat avoids:",
      "options": [
        "Sorting transactions.",
        "Rule generation.",
        "Repeated database scans for support counting.",
        "Calculating lift values."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Once tidsets are built, Eclat computes supports via intersections without revisiting the database."
    },
    {
      "id": 22,
      "questionText": "In the context of Eclat, what does 'depth-first lattice traversal' mean?",
      "options": [
        "Exploring all children of a node before moving deeper.",
        "Recursively exploring itemset extensions down each path.",
        "Randomly selecting items at each level.",
        "Building a balanced tree of transactions."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Depth-first traversal means extending one prefix path completely before exploring siblings, reducing memory overhead."
    },
    {
      "id": 23,
      "questionText": "If item A has tidset {1,2,3} and item B has tidset {2,3,4}, what is the support count of {A,B}?",
      "options": [
        "3",
        "1",
        "2",
        "4"
      ],
      "correctAnswerIndex": 2,
      "explanation": "The intersection {2,3} gives a support count of 2 for the itemset {A,B}."
    },
    {
      "id": 24,
      "questionText": "Eclat’s vertical representation is especially suitable for:",
      "options": [
        "Real-time rule generation on disk.",
        "Text data without tokenization.",
        "Handling continuous variables directly.",
        "In-memory computation of support values."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Since intersections are memory-based operations, Eclat excels when data can be fully loaded in memory."
    },
    {
      "id": 25,
      "questionText": "When constructing the vertical format, how many times must the dataset be scanned?",
      "options": [
        "Once per itemset size.",
        "Twice for each iteration.",
        "Once to build all initial tidsets.",
        "Continuously throughout execution."
      ],
      "correctAnswerIndex": 2,
      "explanation": "A single initial scan builds the tidsets for all 1-itemsets; further intersections happen in memory."
    },
    {
      "id": 26,
      "questionText": "Which situation would make Eclat less efficient?",
      "options": [
        "Binary attributes.",
        "Extremely large number of transactions causing huge tidsets.",
        "Small sparse datasets.",
        "Uniform transaction sizes."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Large tidsets increase intersection costs and memory use, reducing Eclat’s advantage."
    },
    {
      "id": 27,
      "questionText": "Which phase directly follows tidset creation in Eclat?",
      "options": [
        "Database compression.",
        "Rule evaluation.",
        "Support threshold tuning.",
        "Candidate generation and intersection phase."
      ],
      "correctAnswerIndex": 3,
      "explanation": "After tidsets are built, Eclat generates larger itemsets by intersecting tidsets of smaller frequent ones."
    },
    {
      "id": 28,
      "questionText": "In practice, how are transaction IDs represented within tidsets?",
      "options": [
        "As hashed key-value pairs.",
        "As floating-point weights.",
        "As random strings.",
        "As sorted integer lists or bit vectors."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Tidsets are usually stored as sorted lists or bit vectors to make intersections faster."
    },
    {
      "id": 29,
      "questionText": "If the intersection of two tidsets results in a set smaller than the minimum support count, what does Eclat do?",
      "options": [
        "Prunes that combined itemset from further exploration.",
        "Retains it for later rule generation.",
        "Stores it in a separate list for validation.",
        "Expands it with other items."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Itemsets that fail the minimum support condition are pruned, preventing wasteful extensions."
    },
    {
      "id": 30,
      "questionText": "Which of the following is NOT a characteristic of Eclat?",
      "options": [
        "Uses vertical database format.",
        "Performs depth-first traversal.",
        "Computes support via tidset intersections.",
        "Requires multiple full database scans."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat avoids multiple database scans; after building the vertical format, all further operations are in-memory."
    },
    {
      "id": 31,
      "questionText": "In Eclat, how is a new k-itemset generated from (k-1)-itemsets?",
      "options": [
        "By sampling transactions.",
        "By performing a union of tidsets.",
        "By concatenating any two frequent itemsets at random.",
        "By combining two (k-1)-itemsets with the same prefix and intersecting their tidsets."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat combines two (k-1)-itemsets that share a prefix, then intersects their tidsets to form the new k-itemset and compute its support."
    },
    {
      "id": 32,
      "questionText": "What role does recursion play in the Eclat algorithm?",
      "options": [
        "It reduces tidsets using hashing.",
        "It explores itemset extensions in depth-first order.",
        "It helps repeatedly rescan the database.",
        "It reorders tidsets randomly."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Recursion allows Eclat to expand each itemset fully before backtracking, enabling efficient depth-first traversal."
    },
    {
      "id": 33,
      "questionText": "Which structure helps avoid redundant intersections in Eclat?",
      "options": [
        "Matrix multiplication cache",
        "Prefix-based partitioning (equivalence classes)",
        "Transaction trees",
        "Hash tables for each transaction"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Eclat groups itemsets by shared prefixes in equivalence classes, avoiding redundant recomputation of common intersections."
    },
    {
      "id": 34,
      "questionText": "When Eclat uses a vertical database format, what is the main computational bottleneck?",
      "options": [
        "Sorting transactions",
        "Database loading time",
        "Tidset intersection cost",
        "Hash computation"
      ],
      "correctAnswerIndex": 2,
      "explanation": "The intersection of large tidsets can be computationally expensive, especially for dense data."
    },
    {
      "id": 35,
      "questionText": "Which of the following methods is sometimes used to optimize Eclat’s performance?",
      "options": [
        "Running breadth-first search",
        "Using K-means for item grouping",
        "Adding random sampling steps",
        "Using bitset representations for tidsets"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Representing tidsets as bit vectors speeds up intersections using bitwise AND operations."
    },
    {
      "id": 36,
      "questionText": "What determines whether two itemsets can be combined in Eclat?",
      "options": [
        "They must have equal support.",
        "Their tidsets must be disjoint.",
        "They must contain at least one identical transaction ID.",
        "They must share the same (k-1)-prefix."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Only itemsets sharing the same prefix (except the last item) are combined to generate larger candidate itemsets."
    },
    {
      "id": 37,
      "questionText": "Which measure directly affects the pruning strength in Eclat?",
      "options": [
        "Number of unique items",
        "Minimum support threshold",
        "Maximum transaction count",
        "Number of recursive calls"
      ],
      "correctAnswerIndex": 1,
      "explanation": "A higher minimum support threshold leads to stronger pruning and fewer intersections."
    },
    {
      "id": 38,
      "questionText": "Eclat’s efficiency decreases when:",
      "options": [
        "Data becomes denser and tidsets grow longer.",
        "The support threshold is high.",
        "Few frequent items exist.",
        "Transactions are short and sparse."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Dense datasets cause long tidsets and heavy intersection overhead, reducing Eclat’s speed advantage."
    },
    {
      "id": 39,
      "questionText": "What happens when two itemsets have identical tidsets in Eclat?",
      "options": [
        "They are pruned immediately.",
        "They are grouped under the same equivalence class.",
        "They form a cycle in recursion.",
        "They are expanded separately."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Identical tidsets imply the same occurrence pattern, so such itemsets can belong to the same equivalence class."
    },
    {
      "id": 40,
      "questionText": "What is the time complexity of intersecting two tidsets of sizes m and n?",
      "options": [
        "O(m + n)",
        "O(1)",
        "O(m × n)",
        "O(log(m + n))"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Tidset intersection is linear in the sum of their sizes since both lists are traversed once."
    },
    {
      "id": 41,
      "questionText": "Which scenario allows Eclat to outperform Apriori the most?",
      "options": [
        "When there are thousands of dense transactions.",
        "When the dataset is small enough to fit in memory and sparse.",
        "When items have negative values.",
        "When data changes every second."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Eclat’s vertical intersections excel for sparse in-memory datasets, where Apriori’s repeated scans are costly."
    },
    {
      "id": 42,
      "questionText": "Why does Eclat use a depth-first approach instead of breadth-first?",
      "options": [
        "To reduce memory footprint during exploration.",
        "To scan the database faster.",
        "To process all itemsets simultaneously.",
        "To handle continuous features."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Depth-first traversal processes fewer itemsets in memory at a time, conserving space compared to breadth-first methods like Apriori."
    },
    {
      "id": 43,
      "questionText": "Which function in Eclat’s pseudo-code triggers recursive calls?",
      "options": [
        "UpdateDatabase()",
        "Eclat(prefix, items)",
        "GenerateRules()",
        "ComputeSupport()"
      ],
      "correctAnswerIndex": 1,
      "explanation": "The recursive function Eclat(prefix, items) drives the exploration of the search tree by extending prefixes."
    },
    {
      "id": 44,
      "questionText": "In Eclat, how is the intersection operation implemented for speed?",
      "options": [
        "By converting to trees first.",
        "Using sorted list merging or bitwise operations.",
        "Using matrix multiplication.",
        "By recursive hashing."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Tidsets are stored as sorted lists or bit vectors so intersections can be computed efficiently using merging or bitwise AND."
    },
    {
      "id": 45,
      "questionText": "Which of the following is TRUE about Eclat’s memory consumption?",
      "options": [
        "It decreases as the dataset becomes denser.",
        "It is independent of minimum support.",
        "It increases with the number and length of tidsets.",
        "It is constant for all dataset sizes."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Memory grows as more frequent itemsets and longer tidsets are stored in memory for intersections."
    },
    {
      "id": 46,
      "questionText": "How does Eclat avoid generating duplicate itemsets?",
      "options": [
        "By random sampling.",
        "By hashing tidsets.",
        "By rescanning the database to check duplicates.",
        "By maintaining lexicographic ordering and prefix-based combination rules."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat uses ordered prefix rules ensuring each combination is produced once without duplication."
    },
    {
      "id": 47,
      "questionText": "Which pruning technique does Eclat rely on?",
      "options": [
        "Random pruning",
        "Post-pruning based on confidence",
        "Anti-monotonic property of support",
        "Entropy-based pruning"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Eclat, like Apriori, uses the anti-monotonic property—if an itemset is infrequent, its supersets are also infrequent."
    },
    {
      "id": 48,
      "questionText": "What happens after intersecting tidsets in Eclat?",
      "options": [
        "The rule confidence is computed immediately.",
        "The resulting support is compared to min_support for pruning.",
        "The database is rescanned.",
        "The transaction IDs are randomized."
      ],
      "correctAnswerIndex": 1,
      "explanation": "After intersection, Eclat checks whether the new itemset’s support meets the threshold to decide if it should be extended further."
    },
    {
      "id": 49,
      "questionText": "In Eclat, frequent itemsets are generated:",
      "options": [
        "Recursively from smaller itemsets by intersection.",
        "Using probabilistic estimates.",
        "By repeated full database scans.",
        "Directly from rules."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Eclat recursively generates larger frequent itemsets by intersecting smaller ones."
    },
    {
      "id": 50,
      "questionText": "When Eclat is implemented with bitsets, which operation becomes highly efficient?",
      "options": [
        "Tidset compression",
        "Itemset sorting",
        "Rule generation",
        "Support counting via bitwise AND"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Bitwise AND allows extremely fast intersections for support counting."
    },
    {
      "id": 51,
      "questionText": "Which of the following best describes the output of an Eclat function call?",
      "options": [
        "A cluster of transactions.",
        "All frequent itemsets generated from the given prefix.",
        "A compressed representation of data.",
        "A set of association rules."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Each recursive call returns all frequent itemsets that extend the provided prefix."
    },
    {
      "id": 52,
      "questionText": "What is the advantage of dividing items into equivalence classes in Eclat?",
      "options": [
        "Removes need for minimum support.",
        "Improves rule confidence calculation.",
        "Reduces redundant comparisons and simplifies recursion.",
        "Adds randomization for exploration."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Grouping items by prefix into equivalence classes avoids recomputing common intersections and streamlines recursion."
    },
    {
      "id": 53,
      "questionText": "How does Eclat differ from FP-Growth in structure?",
      "options": [
        "FP-Growth relies on vertical format like Eclat.",
        "Both use identical tree-based compression.",
        "Eclat builds frequent pattern trees.",
        "Eclat uses tidsets, while FP-Growth uses tree compression."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat uses a vertical representation with tidsets, whereas FP-Growth compresses transactions into an FP-tree."
    },
    {
      "id": 54,
      "questionText": "Which scenario can cause exponential growth in Eclat’s intermediate itemsets?",
      "options": [
        "High minimum support threshold.",
        "Small number of transactions.",
        "Very low support threshold with many co-occurring items.",
        "Sparse data with few frequent items."
      ],
      "correctAnswerIndex": 2,
      "explanation": "When min_support is low and many items co-occur, combinations explode exponentially."
    },
    {
      "id": 55,
      "questionText": "Why might Eclat be unsuitable for streaming or dynamic data?",
      "options": [
        "It continuously rescans updated transactions.",
        "It relies on random updates.",
        "It uses time-dependent hashing.",
        "It assumes a static dataset loaded in memory."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat’s vertical format assumes static data; incremental updates would require rebuilding tidsets."
    },
    {
      "id": 56,
      "questionText": "Which of these determines how deep the recursion tree can grow in Eclat?",
      "options": [
        "Lift ratio",
        "Minimum confidence value",
        "Transaction length",
        "Number of unique frequent items"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Recursion depth depends on how many unique frequent items can be combined while staying above support threshold."
    },
    {
      "id": 57,
      "questionText": "In which step does Eclat check for minimum support satisfaction?",
      "options": [
        "After each tidset intersection",
        "During data loading",
        "After all itemsets are generated",
        "Before recursion starts"
      ],
      "correctAnswerIndex": 0,
      "explanation": "After every intersection, Eclat immediately checks if the new itemset meets the minimum support threshold."
    },
    {
      "id": 58,
      "questionText": "What effect does increasing the minimum support threshold have on Eclat’s runtime?",
      "options": [
        "It doubles the recursion depth.",
        "It generally decreases runtime due to fewer frequent itemsets.",
        "It has no effect on runtime.",
        "It increases runtime significantly."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Higher min_support reduces the search space and the number of intersections, making Eclat faster."
    },
    {
      "id": 59,
      "questionText": "What is the function of the prefix parameter in Eclat’s recursive procedure?",
      "options": [
        "To count the total support values.",
        "To track random seeds for recursion.",
        "To store all transaction IDs.",
        "To maintain the current itemset being extended."
      ],
      "correctAnswerIndex": 3,
      "explanation": "The prefix holds the current partial itemset, and recursion extends it by adding new items."
    },
    {
      "id": 60,
      "questionText": "Which operation is repeatedly used to grow the search tree in Eclat?",
      "options": [
        "Transaction duplication",
        "Support averaging",
        "Tidset intersection and prefix extension",
        "Database normalization"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Eclat repeatedly extends prefixes by intersecting tidsets to form new itemsets."
    },
    {
      "id": 61,
      "questionText": "Why is Eclat considered deterministic?",
      "options": [
        "It prunes based on probability.",
        "It chooses prefixes randomly.",
        "It always produces the same frequent itemsets for the same input and parameters.",
        "It uses random sampling internally."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Given identical input and min_support, Eclat always produces the same frequent itemsets."
    },
    {
      "id": 62,
      "questionText": "Which data type is most efficient for tidsets during large intersections?",
      "options": [
        "Bit vectors",
        "Tuples",
        "JSON objects",
        "Strings"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Bit vectors enable extremely fast intersections via bitwise AND operations, ideal for large datasets."
    },
    {
      "id": 63,
      "questionText": "What is one drawback of Eclat’s recursive nature?",
      "options": [
        "It requires database rescan.",
        "It produces inaccurate supports.",
        "It cannot prune infrequent itemsets.",
        "It can cause stack overflow for deep lattices."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Excessive recursion depth on large datasets can cause stack overflow or high memory consumption."
    },
    {
      "id": 64,
      "questionText": "Which type of datasets pose the greatest challenge for Eclat?",
      "options": [
        "Datasets with missing values.",
        "Sparse datasets with few items per transaction.",
        "Dense datasets with many co-occurring items.",
        "Datasets with sorted transactions."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Dense datasets create long tidsets, making intersections slower and memory-heavy."
    },
    {
      "id": 65,
      "questionText": "How can the intersection cost be reduced in Eclat?",
      "options": [
        "By intersecting smaller tidsets first.",
        "By sorting tidsets alphabetically.",
        "By skipping prefix pruning.",
        "By random sampling."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Intersecting smaller tidsets first minimizes computation since intersections quickly become empty."
    },
    {
      "id": 66,
      "questionText": "Which optimization can be used when item IDs are represented as integers?",
      "options": [
        "Applying matrix decomposition.",
        "Using bitmask operations for tidset intersections.",
        "Sorting transactions lexicographically.",
        "Using entropy-based grouping."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Integer-based bitmasks allow direct use of bitwise AND for fast intersections."
    },
    {
      "id": 67,
      "questionText": "How does Eclat differ from Apriori in support counting?",
      "options": [
        "Both use identical scanning techniques.",
        "Apriori uses intersection but Eclat uses addition.",
        "Eclat computes support from rule confidence.",
        "Eclat uses tidset intersection, Apriori scans the database repeatedly."
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat calculates support through tidset intersection; Apriori counts by rescanning transactions."
    },
    {
      "id": 68,
      "questionText": "What is the effect of increasing the number of frequent items in Eclat?",
      "options": [
        "It prunes more itemsets.",
        "It reduces memory consumption.",
        "It simplifies equivalence classes.",
        "It increases recursion depth and intersection overhead."
      ],
      "correctAnswerIndex": 3,
      "explanation": "More frequent items increase potential combinations, deepening recursion and raising intersection costs."
    },
    {
      "id": 69,
      "questionText": "Why does Eclat perform better with sparse datasets?",
      "options": [
        "It avoids recursion entirely.",
        "It converts sparse data to dense form.",
        "Tidsets are short, making intersections faster.",
        "It skips prefix formation."
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sparse datasets have shorter tidsets, reducing intersection and memory cost."
    },
    {
      "id": 70,
      "questionText": "Which factor most directly controls Eclat’s memory usage?",
      "options": [
        "Number and size of tidsets stored at each recursion level.",
        "Support confidence ratio.",
        "Rule lift values.",
        "Transaction ordering."
      ],
      "correctAnswerIndex": 0,
      "explanation": "Memory usage grows with how many tidsets and intersections are maintained at once during recursion."
    },
    {
      "id": 71,
      "questionText": "In a dataset with extremely long transactions, which strategy can reduce Eclat’s memory overhead?",
      "options": [
        "Increase recursion depth",
        "Use tidset compression or bit vectors",
        "Scan the database repeatedly",
        "Use a random sampling of transactions"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Tidset compression (e.g., using bit vectors or run-length encoding) reduces memory usage for long transactions while enabling fast intersections."
    },
    {
      "id": 72,
      "questionText": "You notice Eclat is slow on a dense dataset with many frequent items. Which modification could improve performance?",
      "options": [
        "Use unordered itemset combinations",
        "Apply a higher minimum support threshold",
        "Switch to breadth-first traversal",
        "Scan the database multiple times"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Raising the minimum support prunes many infrequent combinations early, reducing intersection computations and speeding up Eclat."
    },
    {
      "id": 73,
      "questionText": "In a scenario where items are added dynamically to the transaction database, what is a limitation of standard Eclat?",
      "options": [
        "It performs online rule generation.",
        "It assumes a static dataset and may need complete tidset reconstruction.",
        "It automatically adjusts tidsets incrementally.",
        "It reduces minimum support for new items."
      ],
      "correctAnswerIndex": 1,
      "explanation": "Standard Eclat does not handle dynamic data efficiently; adding items typically requires rebuilding the vertical tidset structure."
    },
    {
      "id": 74,
      "questionText": "When representing tidsets as bit vectors, which operation allows O(1) support counting?",
      "options": [
        "Bitwise AND followed by counting set bits",
        "Sorting the bit vector",
        "Union of two bit vectors",
        "Recursive traversal of each bit"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Bitwise AND quickly computes intersections; counting the set bits gives support efficiently."
    },
    {
      "id": 75,
      "questionText": "During Eclat execution, you notice some tidset intersections are empty early. What optimization does this allow?",
      "options": [
        "Resort transactions alphabetically",
        "Continue intersection for completeness",
        "Early pruning of all supersets",
        "Ignore the minimum support threshold"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Empty intersections indicate infrequent combinations, allowing immediate pruning of all supersets."
    },
    {
      "id": 76,
      "questionText": "Which real-world dataset is likely to favor Eclat over Apriori?",
      "options": [
        "Dense sensor readings updated every second",
        "Continuous time-series stock data",
        "Sparse supermarket transactions with thousands of items",
        "Text documents requiring TF-IDF computation"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sparse transactional datasets allow Eclat to efficiently compute intersections without repeated scans, outperforming Apriori."
    },
    {
      "id": 77,
      "questionText": "You want to parallelize Eclat on a multi-core machine. Which step is most suitable for parallel execution?",
      "options": [
        "Support threshold tuning",
        "Initial transaction sorting",
        "Tidset construction from the database",
        "Recursive exploration of different equivalence classes"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Different equivalence classes are independent and can be explored in parallel safely without shared state conflicts."
    },
    {
      "id": 78,
      "questionText": "When using bitset representation for tidsets, what is the space complexity for n transactions?",
      "options": [
        "O(n) bits per itemset",
        "O(log n) bits per itemset",
        "O(n^2) bits per itemset",
        "O(1) bits per itemset"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Each tidset uses one bit per transaction, so the space complexity is O(n) bits per itemset."
    },
    {
      "id": 79,
      "questionText": "Which scenario can lead to stack overflow in Eclat?",
      "options": [
        "Deep recursion from many frequent items with low minimum support",
        "Datasets with short transactions",
        "High support threshold pruning most combinations",
        "Sparse datasets with few items"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Excessive recursion depth due to low min_support and numerous frequent items can exhaust the call stack."
    },
    {
      "id": 80,
      "questionText": "You observe that Eclat’s runtime increases drastically with a small decrease in min_support. Why?",
      "options": [
        "Database scanning is skipped",
        "Tidset intersections become faster",
        "Transactions get shorter",
        "More itemsets become frequent, increasing intersections exponentially"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Lowering min_support drastically increases the number of candidate itemsets, leading to more intersections and deeper recursion."
    },
    {
      "id": 81,
      "questionText": "Which optimization can reduce the number of intersections in Eclat?",
      "options": [
        "Randomly shuffling items",
        "Ordering items by ascending tidset size before combining",
        "Expanding larger tidsets first",
        "Ignoring the prefix structure"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Intersecting smaller tidsets first reduces computation since intersections often become empty quickly."
    },
    {
      "id": 82,
      "questionText": "You are comparing Eclat with FP-Growth. Which of the following is true?",
      "options": [
        "Eclat uses vertical tidsets; FP-Growth uses compressed FP-trees",
        "Both use depth-first vertical tidsets",
        "Eclat builds a prefix tree like FP-Growth",
        "FP-Growth uses horizontal scanning only"
      ],
      "correctAnswerIndex": 0,
      "explanation": "FP-Growth compresses data into a tree structure, whereas Eclat keeps vertical tidsets for intersections."
    },
    {
      "id": 83,
      "questionText": "How does Eclat handle items with extremely high support?",
      "options": [
        "It reduces their support artificially",
        "It combines them with others first to generate large frequent itemsets",
        "It ignores them",
        "It prunes them from equivalence classes"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High-support items are prioritized in combinations since they are more likely to generate larger frequent itemsets."
    },
    {
      "id": 84,
      "questionText": "In practical implementations, what is a common way to represent tidsets for memory efficiency?",
      "options": [
        "String lists",
        "Bit arrays or compressed sparse structures",
        "Nested JSON objects",
        "Linked lists with pointers"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Bit arrays or sparse structures allow fast intersection and efficient memory usage."
    },
    {
      "id": 85,
      "questionText": "You are using Eclat on a dataset with 1 million transactions. Which design choice is critical?",
      "options": [
        "Sorting items lexicographically",
        "Using breadth-first traversal",
        "Efficient tidset representation and pruning",
        "Increasing recursion stack depth"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Memory-efficient tidset storage and aggressive pruning are essential to handle very large datasets."
    },
    {
      "id": 86,
      "questionText": "When combining two tidsets A and B, what is the worst-case size of the intersection?",
      "options": [
        "Maximum of |A| and |B|",
        "Minimum of |A| and |B|",
        "Sum of |A| and |B|",
        "Always 1"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Intersection cannot exceed the size of the smaller set."
    },
    {
      "id": 87,
      "questionText": "Scenario: Dataset has 10,000 items with many co-occurring sets. Which problem is most likely for standard Eclat?",
      "options": [
        "Support calculation becomes approximate",
        "Memory explosion due to massive number of tidsets",
        "Database scanning becomes slow",
        "Prefix ordering fails"
      ],
      "correctAnswerIndex": 1,
      "explanation": "A large number of frequent items leads to exponential growth in tidsets, consuming huge memory."
    },
    {
      "id": 88,
      "questionText": "Eclat’s depth-first approach benefits which type of datasets the most?",
      "options": [
        "Non-transactional datasets",
        "High-dimensional dense datasets",
        "Streaming datasets",
        "Sparse datasets that fit in memory"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Depth-first minimizes memory usage for in-memory processing of sparse datasets."
    },
    {
      "id": 89,
      "questionText": "Which of the following Eclat variants improves performance for very large datasets?",
      "options": [
        "Randomized support counting",
        "Sequential depth-first Eclat",
        "Breadth-first non-pruned Eclat",
        "Parallel or distributed Eclat"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Parallel or distributed Eclat partitions equivalence classes among nodes, enabling scalable computation."
    },
    {
      "id": 90,
      "questionText": "You need to compute frequent itemsets with extremely low support in a dataset with millions of transactions. What is a practical limitation?",
      "options": [
        "Support counting is approximate",
        "Prefix ordering fails",
        "Exponential number of candidate itemsets leads to infeasible memory and runtime",
        "Tidsets will become empty"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Low support leads to combinatorial explosion in frequent itemsets, making Eclat computationally expensive."
    },
    {
      "id": 91,
      "questionText": "Scenario: Some items occur in almost every transaction. How does this affect Eclat?",
      "options": [
        "They shorten recursion depth",
        "These high-support items produce many intersections, increasing computation",
        "They reduce memory usage",
        "They are pruned automatically"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High-support items combine with many other items, creating large intersections and increasing computational cost."
    },
    {
      "id": 92,
      "questionText": "When implementing Eclat, which data structure supports fast intersection and minimal memory usage?",
      "options": [
        "Linked lists",
        "String arrays",
        "Compressed bitsets or Roaring bitmaps",
        "JSON objects"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Compressed bitsets allow efficient intersection while keeping memory usage low."
    },
    {
      "id": 93,
      "questionText": "Scenario: You are processing transactions with very few items per transaction. Eclat’s performance is:",
      "options": [
        "Ineffective due to missing pruning",
        "Very slow due to many combinations",
        "Very fast due to small tidsets and fewer intersections",
        "Memory intensive"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sparse transactions result in short tidsets and fast intersections, improving performance."
    },
    {
      "id": 94,
      "questionText": "Which real-world application could benefit most from Eclat?",
      "options": [
        "Image classification using CNNs",
        "Text summarization using TF-IDF",
        "Real-time stock price prediction",
        "Market basket analysis with sparse transactions"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat excels at mining frequent itemsets in sparse transaction datasets, such as shopping baskets."
    },
    {
      "id": 95,
      "questionText": "Scenario: Implementing Eclat with extremely high recursion depth leads to stack overflow. How can this be addressed?",
      "options": [
        "Sort items lexicographically",
        "Increase transaction length",
        "Convert recursion to iterative stack-based traversal",
        "Decrease minimum support to zero"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Using an explicit stack avoids recursion limits and prevents stack overflow."
    },
    {
      "id": 96,
      "questionText": "Eclat uses the anti-monotone property of support. Which statement correctly describes this property?",
      "options": [
        "Confidence is independent of support",
        "If an itemset is infrequent, all supersets are also infrequent",
        "Intersection size is always constant",
        "Support increases with larger itemsets"
      ],
      "correctAnswerIndex": 1,
      "explanation": "The anti-monotone property allows pruning: if an itemset fails min_support, its supersets are not explored."
    },
    {
      "id": 97,
      "questionText": "In distributed Eclat, which challenge is critical?",
      "options": [
        "Sorting local transactions",
        "Compressing bitsets",
        "Calculating rule confidence locally",
        "Synchronizing tidset intersections across nodes"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Distributed execution requires careful coordination to correctly compute intersections spanning multiple partitions."
    },
    {
      "id": 98,
      "questionText": "Scenario: Dataset contains many high-cardinality items. Which effect on Eclat is expected?",
      "options": [
        "Large number of candidate itemsets and high memory usage",
        "Fewer intersections are needed",
        "Recursion depth decreases",
        "Tidsets become smaller"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High-cardinality items increase potential combinations, generating many candidate itemsets and consuming memory."
    },
    {
      "id": 99,
      "questionText": "Scenario: You want to mine only maximal frequent itemsets using Eclat. What adjustment is required?",
      "options": [
        "Decrease minimum support to zero",
        "Use breadth-first traversal",
        "Store all tidsets regardless of support",
        "Track only itemsets whose supersets are infrequent"
      ],
      "correctAnswerIndex": 3,
      "explanation": "To get maximal frequent itemsets, Eclat must check that no superset is frequent before reporting an itemset."
    },
    {
      "id": 100,
      "questionText": "Which scenario demonstrates the biggest performance difference between Eclat and Apriori?",
      "options": [
        "Continuous time-series data",
        "Dense dataset with few transactions",
        "Small dataset with fewer than 10 items",
        "Sparse dataset with thousands of items where Eclat avoids multiple database scans"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Eclat avoids repeated database scans in sparse, high-dimensional datasets, outperforming Apriori significantly."
    }
  ]
}