An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform

Bay Vo,Bao Huynh

doi:10.1155/2018/8487641

Bay Vo, Bao Huynh

Open Access

https://doi.org/10.1155/2018/8487641

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Abstract

Mining erasable itemset (EI) is an attracting field in frequent pattern mining, a wide tool used in decision support systems, which was proposed to analyze and resolve economic problem. Many approaches have been proposed recently, but the complexity of the problem is high which leads to time-consuming and requires large system resources. Therefore, this study proposes an effective method for mining EIs based on multicore processors (pMEI) to improve the performance of system in aspect of execution time to achieve the better user experiences. This method also solves some limitations of parallel computing approaches in communication, data transfers, and synchronization. A dynamic mechanism is also used to resolve the load balancing issue among processors. We compared the execution time and memory usage of pMEI to other methods for mining EIs to prove the effectiveness of the proposed algorithm. The experiments show that pMEI is better than MEI in the execution time while the memory usage of both methods is the same.

Highlights

Data mining is an interesting field that has attracted many experts because of the huge amounts of data that were collected every day and the need to transfer such data into useful information to use in intelligence systems such as recommendation systems, decision making, and expert systems
Some issues related to FP mining has been proposed such as maximal frequent patterns [6], top-k cooccurrence items with sequential pattern [7], weightedbased patterns [8], periodic-frequent patterns [9], and their applications [10, 11]
MEI uses a divide-and-conquer approach associated with the difference of pidsets for mining erasable itemset (EI) to improve the memory usage and runtime

Summary

Introduction

Data mining is an interesting field that has attracted many experts because of the huge amounts of data that were collected every day and the need to transfer such data into useful information to use in intelligence systems such as recommendation systems, decision making, and expert systems. Data mining has been widely used in market basket analysis, manufacturing engineering, financial banking, bioinformatics and future healthcare, and so on. The mining frequent pattern (FP) has a vital position in many data mining fields including association rule mining [1], clustering [2], and text mining [3]. Mining FP is to find all patterns that have the frequency satisfying the user-given threshold. There are many methods [4, 5] for mining FPs in recent years. Some issues related to FP mining has been proposed such as maximal frequent patterns [6], top-k cooccurrence items with sequential pattern [7], weightedbased patterns [8], periodic-frequent patterns [9], and their applications [10, 11]

Methods

Results

Conclusion