Comparative Analysis of On-Shelf Utility Mining Algorithm

Authors

  • Dr. S. Vijayarani  Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
  • C. Sivamathi   Ph. D Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
  • V. Jeevika Tharini  PG student, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Keywords:

Utility mining, On-Shelf utility mining, temporal database, relative utility, periodical utility.

Abstract

Data mining is a process of retrieving previously unknown and needed patterns from database. Utility mining is one of the important fields in data mining. Utility mining is a process of finding high utility itemsets from a database. An item is termed as high utility item if the item’s utility is more than minimum threshold value. Utility of an item is based on user’s interest or preference. Recently, temporal data mining has become a core data processing technique to deal with the changing data. On-shelf utility mining includes the on-shelf time period of item and gets the exact utility values of itemsets in temporal database. In traditional on-shelf utility mining, profits of all items in databases are considered as positive values. However, in real applications, some items may have negative profit. In this work both FOSHU (Faster On-Shelf High Utility) and TS-HOUN (Three-Scan Algorithm for Mining On-shelf High Utility Itemsets with Negative profit) algorithms are compared and their performances were measured.

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International Journal of Scientific Research in Science and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 5, March-April-2018

Section

Research Articles

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How to Cite

[1]
Dr. S. Vijayarani, C. Sivamathi , V. Jeevika Tharini, " Comparative Analysis of On-Shelf Utility Mining Algorithm, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 5, pp.1678-1684, March-April-2018.