Research Article
Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 122 - Issue 12 |
| Published: July 2015 |
| Authors: Uma Dave, Jayna Shah |
10.5120/21752-5031
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Uma Dave, Jayna Shah . Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset. International Journal of Computer Applications. 122, 12 (July 2015), 22-28. DOI=10.5120/21752-5031
@article{ 10.5120/21752-5031,
author = { Uma Dave,Jayna Shah },
title = { 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 },
doi = { 10.5120/21752-5031 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Uma Dave
%A Jayna Shah
%T Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset%T
%J International Journal of Computer Applications
%V 122
%N 12
%P 22-28
%R 10.5120/21752-5031
%I Foundation of Computer Science (FCS), NY, USA
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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