NDLI: Clustering Data Streams Using Mass Estimation

Content Provider	IEEE Xplore Digital Library
Author	Sabau, A.S.
Copyright Year	2013
Description	Author affiliation: Fac. of Math. & Comput. Sci., Univ. of Pitesti, Pitesti, Romania (Sabau, A.S.)
Abstract	The explosive growth of data generation, storage and analysis within the last decade has led to extensive research towards stream mining algorithms. The existing stream clustering literature contains both adaptation of classical methods as well as novel ones trying to address space and time scalability issues arising from dealing with high volume, high velocity information assets. This paper presents MaStream, a novel stream clustering algorithm experiencing constant space complexity and average case sub-linear time complexity. The algorithm makes use of mass estimation as an alternative to density estimation without employing any distance measure making it highly adaptable to both low and high dimensional data streams. Employing an evolving ensemble of h:d-Trees, the algorithm identifies arbitrary shaped clusters while handling both noise and outliers without a priori information such as total number of clusters. Experimental results over a series of both synthetic and real datasets illustrate the algorithm performance.
Starting Page	289
Ending Page	295
File Size	548896
Page Count	7
File Format	PDF
ISBN	9781479930357
e-ISBN	9781479930364
DOI	10.1109/SYNASC.2013.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-23
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis mass-based clustering Clustering algorithms Estimation stream clustering Vegetation Data models Partitioning algorithms Data mining clustering ensemble
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 Libarray 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

An ensemble learning approach for data stream clustering

Incremental clustering for categorical data using clustering ensemble

Hybrid Ensemble Classifier for Stream Data

A high-dimensional data stream clustering algorithm based on damped window and pruning list tree

Improving the quality of clustering using cluster ensembles

Ensemble based data stream mining with recalling and forgetting mechanisms

Document clustering using gravitational ensemble clustering

Clustering based - A FAST algorithm on high dimensional data

A method for evolving data streams

Clustering Data Streams Using Mass Estimation

Similar Documents

An ensemble learning approach for data stream clustering

Incremental clustering for categorical data using clustering ensemble

Hybrid Ensemble Classifier for Stream Data

A high-dimensional data stream clustering algorithm based on damped window and pruning list tree

Improving the quality of clustering using cluster ensembles

Ensemble based data stream mining with recalling and forgetting mechanisms

Document clustering using gravitational ensemble clustering

Clustering based - A FAST algorithm on high dimensional data

A method for evolving data streams

Clustering Data Streams Using Mass Estimation