Fast Frequent Itemset Mining using Compressed Data Representation
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Summary
This paper describes a more efficient algorithm for mining complete frequent itemsets from typical data sets using a compressed prefix tree and presents performance comparisons of the algorithm against the fastest Apriori algorithm, Eclat, and FP-Growth.
- Type
- article
- Published
- 2003-01-01
- Cited by
- 13
- References
- 7
- OpenAlex
- https://openalex.org/W112999697
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17631919
Keywords
Association rule learning, Computer science, Data mining, Apriori algorithm, Database transaction
References
- H-mine: hyper-structure mining of frequent patterns in large databases
- CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth
- Mining frequent patterns without candidate generation
- Scalable Algorithms for Association Mining
- Mining frequent itemsets with convertible constraints
- Mining association rules between sets of items in large databases
- Fast Algorithms for Mining Association Rules
Cited by
- Discovering of Frequent Itemsets with CP-mine Algorithm
- Efficient Mining of Long Frequent Patterns from Very Large Dense Datasets
- Algorithms for mining frequent itemsets in static and dynamic datasets
- Efficient mining frequent itemsets algorithms
- Conceptual Mapping of Risk Management to Data Mining
- CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
- Development and Application of An Adaptive Web Site Construction Algorithm
- A Frequent Item Graph Approach for Discovering Frequent Itemsets
- Techniques for improving clustering and association rules mining from very large transactional databases
- A New Approach for Mining Frequent K-itemset
- Effective Sampling for Mining Association Rules
- Improving the Efficiency of Frequent Pattern Mining by Compact Data Structure Design
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