Mining frequent itemsets using the N-list and subsume concepts

Vo, Bay, Le, Tuong, Coenen, Frans ORCID: 0000-0003-1026-6649 and Hong, Tzung-Pei (2016) Mining frequent itemsets using the N-list and subsume concepts. International Journal of Machine Learning and Cybernetics, 7 (2). pp. 253-265.

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Abstract

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.

Item Type:	Article
Additional Information:	## TULIP Type: Articles/Papers (Journal) ##
Depositing User:	Symplectic Admin
Date Deposited:	07 Feb 2017 15:05
Last Modified:	19 Jan 2023 07:19
DOI:	10.1007/s13042-014-0252-2
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3005599