{ "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." } ] }