CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets

By Jian Pei, Jiawei Han, Runying Mao. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000

984 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/586ed71b41362ef55e92475ff063a753e8536afe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

DATA MINING TECHNIQUES (2003, methodology): “Another promising direction was the development of methods to mine maximal [9, 15, 32, 49] and closed itemsets [59, 61, 83].”
On Private Scalar Product Computation for Privacy-Preserving Data Mining (2004, background): “Empirical results [ PHM00 ] on standard data mining benchmarks indicate that can range from 2 to 100 depending on the frequency threshold, when the database contains some highly correlated items.”
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets (2004, unknown): “これらの問題に対しては,非常に多くのアルゴリズムが提案されている [1, 2, 3, 8, 9, 14, 18, 17, 20, 21].”
Mining top-k frequent closed patterns without minimum support (2002, background): “Thus we conclude that for frequent pattern mining, mining top-k frequent closed patterns without min support is more preferable than the traditional min support-based mining.”
Frequent pattern mining: current status and future directions (2007, methodology): “Web access patterns via association rule mining in web logs were proposed by (Chen et al. 1996; Pei et al. 2000; Srivastava et al. 2000; Punin and Krishnamoorthy 2001).”
CLOSET+: searching for the best strategies for mining frequent closed itemsets (2003, methodology): “, [14, 5, 18]) clearly elaborate that the depth-first search methods are usually more efficient than the breadth-first search methods like A-close.”
Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002, background): “University of New York at Buffalo, jianpei@cse.buffalo. edü IBM T. J. Watson Research Center, psyu@us.ibm.com”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, background): “These datasets should be representative, as recent research papers [2, 3, 4, 11, 10, 8, 9], use these or similar datasets.”
Fast algorithms for frequent itemset mining using FP-trees (2005, methodology): “Zaki and Hsiao [34] proposed a depth-first algorithm, CHARM, for CFI mining.”
MAFIA: a maximal frequent itemset algorithm (2005, background): “Smaller alternatives to FI that still contain enough information for the second phase have been proposed, including the set of frequent closed itemsets FCI [12], [13], [14].”