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Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 12
Year of Publication: 2015
Authors:
Uma Dave
Jayna Shah
10.5120/21752-5031

Uma Dave and Jayna Shah. Article: Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset. International Journal of Computer Applications 122(12):22-28, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Uma Dave and Jayna Shah},
	title = {Article: Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {12},
	pages = {22-28},
	month = {July},
	note = {Full text available}
}

Abstract

Frequent Pattern mining is modified by Sequential Pattern Mining to consider time regularity which is further enhanced to high utility sequential pattern mining (HUS) by incorporating utility into sequential pattern mining for business value and impact. In the process of mining HUS, when new sequences are added into the existing database the whole procedure of mining HUS starts from the scratch, in spite of mining HUS only from incremental sequences. This results in excess of time as well as efforts. So in this paper an incremental algorithm is proposed to mine HUS from the Incremental Database. Experimental results show that the proposed algorithm executes faster than existing PHUS algorithm resulting in saving of time as well as efforts.

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