Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 7, Issue 2, Pages 253-265Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-014-0252-2
Keywords
Data mining; Pattern mining; Frequent itemset; N-list; Subsume
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Funding
- Foundation for Science and Technology Development of Ton Duc Thang University (FOSTECT) [FOSTECT.2014.BR.07]
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Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which in most cases outperforms other current state-of-the-art algorithms, has been presented. This paper proposes an improved version of PrePost, the N-list and Subsume-based algorithm for mining Frequent Itemsets (NSFI) algorithm that uses a hash table to enhance the process of creating the N-lists associated with 1-itemsets and an improved N-list intersection algorithm. Furthermore, two new theorems are proposed for determining the subsume index of frequent 1-itemsets based on the N-list concept. Using the subsume index, NSFI can identify groups of frequent itemsets without determining the N-list associated with them. The experimental results show that NSFI outperforms PrePost in terms of runtime and memory usage and outperforms dEclat in terms of runtime.
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