CHARM: An Efficient Algorithm for Closed Itemset Mining

By Mohammed J. Zaki, Ching-Jui Hsiao. SDM, 2002

1,203 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3e3b17daaf4e7101527f4d34722db6e4d8e2bc34

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In Computational Theory and Mathematics

In Information Systems

In Signal Processing

Citations in the corpus

Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods (2018, methodology): “ordering [34].”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “Frequent pattern mining has been a focused theme in data mining research with a large number of scalable methods proposed for mining various kinds of patterns including itemsets [2, 10, 27], sequences [3, 16, 26] and graphs [11, 22].”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, methodology): “These datasets should be representative, as recent research papers [2, 3, 4, 11, 10, 8, 9], use these or similar datasets.”
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets (2004, neutral): “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.”
A Survey of Sequential Pattern Mining (2017, background): “Algorithms for discovering concise representations of patterns are generally inspired by similar work on itemset mining such as for the discovery of closed itemsets [65, 106, 120, 83, 7, 107], maximal itemsets [106], and generator itemsets [101, 33, 103].”
Review and Comparison of Associative Classification Data Mining Approaches (2014, positive): “ACCF employs the association rule discovery algorithm Charm [34] to reduce the size of the resulting classifiers in AC.”
Survey on Frequent Pattern Mining (2003, methodology): “Recently, Zaki and Gouda [42, 43] proposed a new approach to efficiently compute the support of an itemset using the vertical database layout.”
Frequent pattern mining: current status and future directions (2007, methodology): “Other closed pattern mining algorithms include CLOSET (Pei et al. 2000), CHARM (Zaki and Hsiao 2002), CLOSET+ (Wang et al. 2003), FPClose (Grahne and Zhu 2003) and AFOPT (Liu et al. 2003).”
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.”
Fast vertical mining using diffsets (2003, methodology): “Methods for mining closed sets include the Apriori-based A-Close method [15], the Closet algorithm based on FP-trees [16] and Charm [23].”
Swarm (2010, background): “Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.”