NDLI: Mining frequent pattern using item-transformation method

Content Provider	IEEE Xplore Digital Library
Author	Tsai-Pin Chu Fan Wu Shih-Wen Chiang
Copyright Year	2005
Description	Author affiliation: Dept. of Manage. of Inf. Syst., Nat. Chung-Cheng Univ., Chia-Yi, Taiwan (Tsai-Pin Chu; Fan Wu; Shih-Wen Chiang)
Abstract	Mining frequent patterns is a fundamental and crucial task in data-mining problems. This paper proposes a novel and simple approach, which does not belong to the candidate generation-and-test approach (for example, the a priori algorithm) and the pattern-growth approach (such as the FP-growth algorithm) two approaches. This approach treats the database as a stream of data and finds the frequent patterns by scanning the database only once. Two versions of the approach (i.e., mapping-table and transformation-function) are provided. Analyses and simulations of the approach are also performed. Analyses show that the transformation-function version is much better than the a priori and FP-growth ones in storage complexity. Simulation results show that the mapping-table version is comparable to the FP-growth algorithm in execution time.
Starting Page	698
Ending Page	706
File Size	177410
Page Count	9
File Format	PDF
ISBN	0769522963
DOI	10.1109/ICIS.2005.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-14
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Transaction databases Itemsets Data structures Information management Management information systems Performance analysis Analytical models Investments Medical services
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Mining frequent closed itemsets using conditional frequent pattern tree

A High Performance Algorithm for Mining Frequent Patterns: LPS-Miner

Mining Periodic-Frequent Itemsets with Approximate Periodicity Using Interval Transaction-Ids List Tree

Considering RFM-values of frequent patterns in transactional databases

MMFI: An Effective Algorithm for Mining Maximal Frequent Itemsets

Mining frequent closed itemsets with the frequent pattern list

An Improved Algorithm for Mining Maximal Frequent Patterns

A New Fast Frequent Itemsets Mining Algorithm Based on Forest

Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

Mining frequent pattern using item-transformation method

Similar Documents

Mining frequent closed itemsets using conditional frequent pattern tree

A High Performance Algorithm for Mining Frequent Patterns: LPS-Miner

Mining Periodic-Frequent Itemsets with Approximate Periodicity Using Interval Transaction-Ids List Tree

Considering RFM-values of frequent patterns in transactional databases

MMFI: An Effective Algorithm for Mining Maximal Frequent Itemsets

Mining frequent closed itemsets with the frequent pattern list

An Improved Algorithm for Mining Maximal Frequent Patterns

A New Fast Frequent Itemsets Mining Algorithm Based on Forest

Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

Mining frequent pattern using item-transformation method