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

Article

Nasreen, Shamila, Azam, Muhammad Awais, Shehzad, Khurram, Naeem, U. and Ghazanfar, Mustansar Ali 2014. Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey. Procedia Computer Science. 37, pp. 109-116.
AuthorsNasreen, Shamila, Azam, Muhammad Awais, Shehzad, Khurram, Naeem, U. and Ghazanfar, Mustansar Ali
Abstract

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.

JournalProcedia Computer Science
Journal citation37, pp. 109-116
ISSN1877-0509
Year2014
PublisherElsevier
Publisher's version
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CC BY-NC-ND
Web address (URL)http://dx.doi.org/10.1016/j.procs.2014.08.019
Publication dates
Print17 Sep 2014
Publication process dates
Deposited29 Sep 2014
Copyright information© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
Additional information

Paper presented at The 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2014) September 22-25, 2014, Halifax, Nova Scotia, Canada

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