NDLI: Parametric Algorithms for Mining Share Frequent Itemsets

Content Provider	Springer Nature Link
Author	Barber, Brock HAMILTON, HOWARD J.
Copyright Year	2001
Abstract	Itemset share, the fraction of some numerical total contributed by items when they occur in itemsets, has been proposed as a measure of the importance of itemsets in association rule mining. The IAB and CAC algorithms are able to find share frequent itemsets that have infrequent subsets. These algorithms perform well, but they do not always find all possible share frequent itemsets. In this paper, we describe the incorporation of a threshold factor into these algorithms. The threshold factor can be used to increase the number of frequent itemsets found at a cost of an increase in the number of infrequent itemsets examined. The modified algorithms are tested on a large commercial database. Their behavior is examined using principles of classifier evaluation from machine learning.
Starting Page	277
Ending Page	293
Page Count	17
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	16
Issue Number	3
e-ISSN	15737675
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2001-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Document Preparation and Text Processing Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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