Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

被引：25

作者：

Nasreen, Shamila ^{[1
]}

Azam, Muhammad Awais ^{[2
]}

Shehzad, Khurram ^{[1
]}

Naeem, Usman ^{[3
]}

Ghazanfar, Mustansar Ali ^{[1
]}

机构：

[1] UET Taxila, Dept Software Engn, Taxila 47080, Pakistan

[2] UET Taxila, Dept Comp Engn, Taxila 47080, Pakistan

[3] Univ East London, Sch Architecture Comp & Engn, London E15 4LZ, England

来源：

5TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS / THE 4TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE / AFFILIATED WORKSHOPS | 2014年 / 37卷

关键词：

Frequent Pattern Growth (FP Growth); Rapid Association Rule Mining (RARM); Data Mining; Frequent Patterns;

D O I：

10.1016/j.procs.2014.08.019

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm's strengths and weaknesses for finding patterns among large item sets in database systems. (C) 2014 The Authors. Published by Elsevier B.V.

引用

页码：109 / +

页数：2

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