NDLI: Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis

Content Provider	IEEE Xplore Digital Library
Author	Raju, G. Thomas, B. Tobgay, S. Kumar, T.S.
Copyright Year	2008
Description	Author affiliation: Mahatma Gandhi Univ. (Thomas, B.) \|\| SCMS Sch. of Technol. & Manage. (Raju, G.) \|\| Sherubtse Coll., Kanglung (Tobgay, S.) \|\| Himachal Pradesh Univ. (Kumar, T.S.)
Abstract	The conventional clustering algorithms in data mining like k-means algorithm have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. The modeling of imprecise and qualitative knowledge, as well as handling of uncertainty at various stages is possible through the use of fuzzy sets. Fuzzy logic is capable of supporting to a reasonable extent, human type reasoning in natural form by allowing partial membership for data items in fuzzy subsets. Integration of fuzzy logic in data mining has become a powerful tool in handling natural data. In this paper we introduce the concept of fuzzy clustering and also the benefits of incorporating fuzzy logic in data mining. Finally this paper provides a comparative analysis of two fuzzy clustering algorithms namely fuzzy c-means algorithm and adaptive fuzzy clustering algorithm.
Starting Page	489
Ending Page	493
File Size	313703
Page Count	5
File Format	PDF
ISBN	9780769534893
DOI	10.1109/ICACTE.2008.199
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-20
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Uncertainty Machine learning algorithms Clustering methods fuzzy logic Partitioning algorithms Data mining fuzzy c-means Fuzzy logic Fuzzy sets Clustering algorithms k-means Iterative algorithms clustering
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 Modified Fuzzy C Means Clustering Using Neutrosophic Logic

A k-Means-Based Projected Clustering Algorithm

Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

Evolutionary fuzzy c-means clustering algorithm

Meteorological Data Analyze Base on K-means Algorithm

Data clustering with modified K-means algorithm

A New Method for Initialising the K-Means Clustering Algorithm

Block fuzzy k-modes clustering algorithm

Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis

Similar Documents

A Modified Fuzzy C Means Clustering Using Neutrosophic Logic

A k-Means-Based Projected Clustering Algorithm

Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

Evolutionary fuzzy c-means clustering algorithm

Meteorological Data Analyze Base on K-means Algorithm

Data clustering with modified K-means algorithm

A New Method for Initialising the K-Means Clustering Algorithm

Block fuzzy k-modes clustering algorithm

Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis