Efficient adaptive frequent pattern mining techniques for market analysis in sequential and parallel systems
Joint Authors
Kuriakose, Sherly
Nedunchezhian, Raju
Source
The International Arab Journal of Information Technology
Issue
Vol. 14, Issue 2 (31 Mar. 2017), pp.175-185, 11 p.
Publisher
Publication Date
2017-03-31
Country of Publication
Jordan
No. of Pages
11
Main Subjects
Abstract EN
The classical applications of Association Rule Mining (ARM) are market analysis, network traffic analysis, and web log analysis where strategic decisions are made by analyzing the frequent itemsets from a large pool of data.
Datasets in such domains are constantly updated and as they require an efficient Frequent Pattern Mining (FPM) algorithm which is capable of extracting the required information.
Several incremental algorithms have been proposed to generate frequent patterns, but they are ineffective with very large datasets and do not provide the user interaction to adjust the minimum support value.
This paper first presents an efficient interactive sequential FPM algorithm that uses the knowledge gained in the previous mining steps to incrementally mine the updated database with fewer complexities.
Then to further reduce the time complexity it proposes an efficient interactive and incremental parallel mining algorithm.
It also prepares incremental frequent patterns, without generating local frequent itemsets with less communication and synchronization overheads.
American Psychological Association (APA)
Kuriakose, Sherly& Nedunchezhian, Raju. 2017. Efficient adaptive frequent pattern mining techniques for market analysis in sequential and parallel systems. The International Arab Journal of Information Technology،Vol. 14, no. 2, pp.175-185.
https://search.emarefa.net/detail/BIM-792070
Modern Language Association (MLA)
Kuriakose, Sherly& Nedunchezhian, Raju. Efficient adaptive frequent pattern mining techniques for market analysis in sequential and parallel systems. The International Arab Journal of Information Technology Vol. 14, no. 2 (2017), pp.175-185.
https://search.emarefa.net/detail/BIM-792070
American Medical Association (AMA)
Kuriakose, Sherly& Nedunchezhian, Raju. Efficient adaptive frequent pattern mining techniques for market analysis in sequential and parallel systems. The International Arab Journal of Information Technology. 2017. Vol. 14, no. 2, pp.175-185.
https://search.emarefa.net/detail/BIM-792070
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 184-185
Record ID
BIM-792070