NDLI: SDR: An algorithm for clustering categorical data using rough set theory

Content Provider	IEEE Xplore Digital Library
Author	Tripathy, B.K. Ghosh, A.
Copyright Year	2011
Description	Author affiliation: SCSE, VIT University, Vellore- INDIA (Tripathy, B.K.; Ghosh, A.)
Abstract	In the present day scenario, there are a large number of clustering algorithms available, to group objects having similar characteristics. But, the implementation of most of these algorithms is challenging due to the fact that most of the datasets involve categorical data values. Again, those algorithms which are capable of handling categorical data are mostly unable to handle uncertainty and some of them are involved with the stability issues. This necessitated the development of algorithms for clustering categorical data while handling uncertainty. In an effort to solve these problems an algorithm, termed MMR [1] was proposed in 2007, which uses the basic rough set theory concepts to deal with the above problem in clustering categorical data. Later in 2009, another algorithm, termed MMeR was proposed [2], which is more efficient than MMR and also has the capability of handling heterogeneous data. In this paper, we further improve MMeR and propose an algorithm, which we call SDR (Standard Deviation Roughness) algorithm It is capable of handling heterogeneous data besides taking care of uncertainty. We establish its efficiency over many other algorithms using well known standard data sets for the purpose of testing and the purity ratio as the measure of efficiency.
Starting Page	867
Ending Page	872
File Size	705136
Page Count	6
File Format	PDF
ISBN	9781424494781
e-ISBN	9781424494774
DOI	10.1109/RAICS.2011.6069433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-22
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis SDR Uncertainty MMR Clustering algorithms Set theory MMeR Classification algorithms Approximation methods Clustering Computer numerical control
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

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