NDLI: Scalable model-based clustering by working on data summaries

Content Provider	IEEE Xplore Digital Library
Author	Huidong Jin Man-Leung Wong Kwong-Sak Leung
Copyright Year	2003
Description	Author affiliation: Dept. of Inf. Syst., Lingnan Univ., Tuen Mun, China (Huidong Jin; Man-Leung Wong)
Abstract	The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources. We present a two-phase scalable model-based clustering framework: first, a large data set is summed up into subclusters; Then, clusters are directly generated from the summary statistics of subclusters by a specifically designed expectation-maximization (EM) algorithm. Taking example for Gaussian mixture models, we establish a provably convergent EM algorithm, EMADS, which embodies cardinality, mean, and covariance information of each subcluster explicitly. Combining with different data summarization procedures, EMADS is used to construct two clustering systems: gEMADS and bEMADS. The experimental results demonstrate that they run several orders of magnitude faster than the classic EM algorithm with little loss of accuracy. They generate significantly better results than other model-based clustering systems using similar computational resources.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	91
Ending Page	98
File Size	1682779
Page Count	8
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1250907
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Databases Statistics Data mining Algorithm design and analysis Iterative algorithms Information systems Scalability Explosives Bridges
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

Scalable clustering with adaptive instance sampling

A New Data Mining Method based on Fusion Clustering Algorithm

Scalable model-based clustering by working on data summaries (2003)

Optimising video summaries using unsupervised clustering

Scalable context-aware role mining with MapReduce

Decision Trees Pruning Algorithm Based on Deficient Data Sets

Frequent patterns-based subspace clustering

Scalable Clustering for Large High-Dimensional Data Based on Data Summarization

A Scalable Hierarchical Clustering Algorithm Using Spark

Scalable model-based clustering by working on data summaries

Similar Documents

Scalable clustering with adaptive instance sampling

A New Data Mining Method based on Fusion Clustering Algorithm

Scalable model-based clustering by working on data summaries (2003)

Optimising video summaries using unsupervised clustering

Scalable context-aware role mining with MapReduce

Decision Trees Pruning Algorithm Based on Deficient Data Sets

Frequent patterns-based subspace clustering

Scalable Clustering for Large High-Dimensional Data Based on Data Summarization

A Scalable Hierarchical Clustering Algorithm Using Spark

Scalable model-based clustering by working on data summaries

Decision Trees Pruning Algorithm Based on Deficient Data Sets