An Approach to Improve Generation of Association Rules in Order to Be Used in Recommenders

An Approach to Improve Generation of Association Rules in Order to Be Used in Recommenders

Hodjat Hamidi (Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran) and Elnaz Hashemzadeh (Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/IJDWM.2017100101
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Abstract

This paper introduces a method for improving the association rules that will also help improve the performance of recommender systems. Combining sampling and parallelism in the process, the proposed method, in addition to help perform the process more quickly, better quality rules will be generated. The proposed method and the dataset will be segmented into a number of smaller sections based on clustering items, and each section is separately sampled. Frequent itemsets and association rules in any section of the selected samples will be found and by gathering and analyzing the results, the quality and number of rules will be evaluated. One of the innovations of this article is the method of sampling of the database. Instead of random sampling, information about the best users and items was isolated. After determining the precise amount of support, the authors extracted the frequent and favorable rules from the selected sample.
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1. Introduction

One important problem in data mining is the discovery of frequent itemsets in a transactional database (Leung, Chan, & Chung, 2006). Frequent itemset mining is a traditional and important problem in data mining. An itemset is frequent if its support is not less than a minimum support specified by users. Traditional frequent itemset mining approaches have mainly considered the problem of mining static transaction databases. In these methods, transactions are stored in secondary storage so that multiple scans over the data can be performed. Frequent patterns, such as frequent itemsets, substructures, sequences term-sets, phrase sets, and sub graphs, generally exist in real-world databases. Identifying frequent itemsets is one of the most important issues faced by the knowledge discovery and data mining community. Frequent itemset mining plays an important role in several data mining fields as association rules (Chandak, Girase, & Mukhopadhyay, 2015; Mousavi et al., 2017; (Bimonte et al., 2017; Hamidi et al., 2016), warehousing (Daraei et al., 2016; Hamidi, 2011, 2012, 2009, 2010, 2011, 2017), correlations, clustering of high-dimensional biological data, and classification (Lin et al., 2002; Hashemzadeh et al., 2016; Mohammadi et al., 2005).

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