International Journal of Computational Intelligence Systems

Volume 8, Issue 3, June 2015, Pages 517 - 529

Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

Authors
Chiranjeevi Manike, Hari Om
Corresponding Author
Chiranjeevi Manike
Available Online 1 June 2015.
DOI
https://doi.org/10.1080/18756891.2015.1023589How to use a DOI?
Keywords
High utility patterns, Data mining, Maximal Patterns, Anti-monotone property, Transaction projection
Abstract
High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDE algorithm to improve the quality and determine exact utilities of maximal patterns.
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
8 - 3
Pages
517 - 529
Publication Date
2015/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2015.1023589How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Chiranjeevi Manike
AU  - Hari Om
PY  - 2015
DA  - 2015/06
TI  - Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams
JO  - International Journal of Computational Intelligence Systems
SP  - 517
EP  - 529
VL  - 8
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1023589
DO  - https://doi.org/10.1080/18756891.2015.1023589
ID  - Manike2015
ER  -