A New Frequent Pattern Mining Algorithm with Weighted Multiple Minimum Supports

Zhang, Haoran; Zhang, Jianwu; Wei, Xuyang; Zhang, Xueyan; Zou, Tengfei; Gao, Yang

doi:10.1080/10798587.2017.1316082

Cited by 4 publications

(1 citation statement)

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“…It reduces the search space and limits the number of frequent patterns generated. However, using single minimum support makes an assumption that all items in the dataset are of the same nature and have the same frequencies which in contrary, not the case in real life applications [8], [9], [10], [11]. In reality, datasets contain items of varying frequently and knowledge pertaining to frequent items can be discovered in the same manner as that pertaining to rare items [12].…”

Section: Introductionmentioning

confidence: 99%

A Modified Apriori Algorithm to Mine Association Rules using Relative Multiple Supports

Pariñas¹

2020

IJATCSE

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Mining frequent itemsets utilizing multiple minimum supports is an essential generalization of the association rule mining problem. Instead of setting a single minimum support for all items, users are allowed to specify different minimum support values to different items. In real applications, using single minimum item support is inadequate since it does not reflect the nature of each item. If single minimum support is set to low, a large number of association rules are generated. On the other hand, if it is set to high, important rules may be lost. In this paper, we proposed an algorithm named Relative Multiple Supports Apriori (RMSApriori) to solve the problem of single multiple support. It is compared with the original Apriori and various experiments are conducted using real datasets. Different values of single minimum support were applied on each dataset for comparison and rules involving frequent and rare items were found. However, the minimum support value has to be set to low resulting in an increase of processing time and space. Experimental results reveal that RMSApriori outperforms Apriori in terms of execution time and memory usage considering the generation of rules not only for frequent items but also for significant rare items.

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Section: Introductionmentioning

confidence: 99%