NDLI: MMFI: An Effective Algorithm for Mining Maximal Frequent Itemsets

Content Provider	IEEE Xplore Digital Library
Author	Shiguang Ju Chen Chen
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Telecommun. Eng., Jiangsu Univ., Jiangsu (Shiguang Ju; Chen Chen)
Abstract	Existing algorithms for mining maximal frequent itemsets have to do superset checking, and some of them using FP-tree have to construct conditional frequent pattern trees recursively. We present a novel algorithm for mining maximal frequent itemsets from a transactional database. In the algorithm, the FP-Tree data structure is used and adapted, and a new strategy called ldquoNBNrdquo (Node By Node) is used for traversing the adapted FP-Tree. Neither superset checking nor constructing conditional frequent pattern trees is needed in the algorithm. We analyze the performance of the algorithm and compare our method with existing algorithms. Our technique works better for mining maximal frequent itemsets. It is also proved by experimental comparison that our algorithm is more fast and efficient.
Starting Page	144
Ending Page	148
File Size	330626
Page Count	5
File Format	PDF
ISBN	9780769531519
DOI	10.1109/ISIP.2008.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finance data mining association rules Data structures Transaction databases Data mining Association rules Computer science maximal frequent itemsets Itemsets Information processing Performance analysis Testing
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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