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Title: Vertical pattern mining algorithm for multiple support thresholds
Authors: Darrab, Sadeq
Ergenç, Belgin
Keywords: Frequent pattern mining
Pattern growth tree
Vertical mining
Multiple support thresholds
Publisher: Elsevier Ltd.
Source: Darrab, S., and Ergenç, B. (2017). Vertical pattern mining algorithm for multiple support thresholds. Procedia Computer Science, 112, 417-426. doi:10.1016/j.procs.2017.08.051
Abstract: Frequent pattern mining is an important task in discovering hidden items that co-occur (itemset) more than a predefined threshold in a database. Mining frequent itemsets has drawn attention although rarely occurring ones might have more interesting insights. In existing studies, to find these interesting patterns (rare itemsets), user defined single threshold should be set low enough but this results in generation of huge amount of redundant itemsets. We present Multiple Item Support-eclat; MIS-eclat algorithm, to mine frequent patterns including rare itemsets under multiple support thresholds (MIS) by utilizing a vertical representation of data. We compare MIS-eclat to our previous tree based algorithm, MISFP-growth28 and another recent algorithm, CFP-growth++22 in terms of execution time, memory usage and scalability on both sparse and dense databases. Experimental results reveal that MIS-eclat and MISFP-growth outperform CFP-growth++ in terms of execution time, memory usage and scalability.
Description: 21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017; Saint Charles Campus of Aix-Marseille UniversityMarseille; France; 6 September 2017 through 8 September 2017
ISSN: 0877-0509
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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