An Optimization to CHARM Algorithm for Mining Frequent Closed Itemsets

Ye, Xin; Feng, Wei; Jiang, Fan; Cheng, Shiduan

doi:10.1109/cit/iucc/dasc/picom.2015.33

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…For the comparison we used DCI-closed, a famous algorithm for mining frequent closed itemsets to be compared with CHARM Algorithm. For performance comparison, we used the original source or object code for DCI-closed provided to us by [10]. Figure 7 illustrate the execution time in the data of both algorithms with different minsup.…”

Section: Resultsmentioning

confidence: 99%

Closed Frequent Itemsets Mining Based on It-Tree

Fakir

Touate

Elayachi

et al. 2020

JMID

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In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting & intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.

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Section: Resultsmentioning

confidence: 99%

Closed Frequent Itemsets Mining Based on It-Tree

Fakir

Touate

Elayachi

et al. 2020

JMID

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show abstract

“…-CHARM simultaneously explores the item space and the transaction space, above a new IT-tree [6,7] search space (tree of itemsets-tidset). On the other hand, most methods use only the item search space.…”

Section: Charm Algorithmmentioning

confidence: 99%

“…-CHARM also uses a new vertical representation of data called diffset [7], for fast frequency calculations. Diffsets keep track of differences in the details of a candidate pattern from its prefix pattern.…”

Section: Charm Algorithmmentioning

confidence: 99%

Untitled

2021

GJCS

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In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This study focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. This paper aims to propose an algorithm for mining closed itemsets, with the construction of an Itemset-Tidset Search Tree (IT-Tree). This algorithm is compared with the Direct Counting & Intersect (DCI) algorithm based on min support and computing time. CHARM needs to store all closed itemsets in the memory. The lower the min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.

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Shared Mental Model Processing in Visualization Technologies: A Review of Fundamental Concepts and a Guide to Future Research in Human-Computer Interaction

Yusoff

Salim

2020

Lecture Notes in Computer Science

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An Optimization to CHARM Algorithm for Mining Frequent Closed Itemsets

Cited by 5 publications

References 15 publications

Closed Frequent Itemsets Mining Based on It-Tree

Closed Frequent Itemsets Mining Based on It-Tree

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Shared Mental Model Processing in Visualization Technologies: A Review of Fundamental Concepts and a Guide to Future Research in Human-Computer Interaction

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