NDLI: Clustering on demand for multiple data streams

Content Provider	IEEE Xplore Digital Library
Author	Bi-Ru Dai Jen-Wei Huang Mi-Yen Yeh Ming-Syan Chen
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Bi-Ru Dai; Jen-Wei Huang; Mi-Yen Yeh; Ming-Syan Chen)
Abstract	In the data stream environment, the patterns generated by the mining techniques are usually distinct at different time because of the evolution of data. In order to deal with various types of multiple data streams and to support flexible mining requirements, we devise in this paper a clustering on demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two major features, namely one data scan for online statistics collection and compact multiresolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. Furthermore, with the multiresolution approximations of data streams, flexible clustering demands can be supported.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	367
Ending Page	370
File Size	146062
Page Count	4
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10060
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistics Data mining Aggregates Association rules Frequency Investments Multiresolution analysis Clustering algorithms
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

A Novel Multivariate Discretization Method for Mining Association Rules

Optimal Grid Exploitation Algorithms for Data Mining

Mining fuzzy association rules in data streams

A method for mining association rules in quantitative and fuzzy data

Fuzzy clustering for categorical multivariate data

Research on Distributive Algorithm of Data Mining with Association Rules

Association Rules for Clustering Algorithms for Data Mining of Temporal Power Ramp Balance

An effective algorithm for mining quantitative associations based on subspace clustering

Adaptive Clustering for Multiple Evolving Streams

Clustering on demand for multiple data streams

Similar Documents

A Novel Multivariate Discretization Method for Mining Association Rules

Optimal Grid Exploitation Algorithms for Data Mining

Mining fuzzy association rules in data streams

A method for mining association rules in quantitative and fuzzy data

Fuzzy clustering for categorical multivariate data

Research on Distributive Algorithm of Data Mining with Association Rules

Association Rules for Clustering Algorithms for Data Mining of Temporal Power Ramp Balance

An effective algorithm for mining quantitative associations based on subspace clustering

Adaptive Clustering for Multiple Evolving Streams

Clustering on demand for multiple data streams