NDLI: An In-depth Analysis of Fuzzy C-Means Clustering for Cellular Manufacturing

Content Provider	IEEE Xplore Digital Library
Author	Jie Li Chao-hsien Chu Yunfeng Wang
Copyright Year	2008
Description	Author affiliation: Sch. of Manage., Hebei Univ. of Technol., Tianjin (Jie Li; Yunfeng Wang) \|\| Sch. of Info. Sci. & Tech., Pennsylvania State Univ., State College, PA (Chao-hsien Chu)
Abstract	Fuzzy c-means (FCM), a well-known clustering algorithm, has been successfully adapted to solve a variety of applications including cellular manufacturing. This paper provides an in-depth analysis on the deficiencies of applying FCM to solve the cell formation (CF) problem in cellular manufacturing and proposes ways of enhancing its performance. A large-scale experiment is conducted to evaluate the effects of different enhancements over FCM. Our study shows that, for CF problem, (1) the proposed distance function has the largest impact on solution quality, followed by the subtractive initialization, (2) the effects of center function and solution selection are not as significant as the formers, and (3) combining the proposed distance function and subtractive initialization with FCM produces the most synergic effects in improving solution quality, while only adding a tolerable amount of computation time.
Starting Page	42
Ending Page	46
File Size	237270
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Group technology manufacturing cell formation Conference management Knowledge management fuzzy c-means cellular manufacturing Technology management Clustering algorithms Production fuzzy clustering Cellular manufacturing Performance analysis Fuzzy systems
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

An improved fuzzy c-means algorithm for manufacturing cell formation

An Algorithm for the Design of Manufacturing Cells

The Library Evaluation Based on the PCA and Fuzzy-c Means

Cell formation with workload data in cellular manufacturing system using genetic algorithm

Advances in Research of Fuzzy C-Means Clustering Algorithm

Application of Fuzzy-C-Means Algorithm Based on Rough Set in Client Subdivision Research

An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem

Evolutionary fuzzy c-means clustering algorithm

Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis

An In-depth Analysis of Fuzzy C-Means Clustering for Cellular Manufacturing

Similar Documents

An improved fuzzy c-means algorithm for manufacturing cell formation

An Algorithm for the Design of Manufacturing Cells

The Library Evaluation Based on the PCA and Fuzzy-c Means

Cell formation with workload data in cellular manufacturing system using genetic algorithm

Advances in Research of Fuzzy C-Means Clustering Algorithm

Application of Fuzzy-C-Means Algorithm Based on Rough Set in Client Subdivision Research

An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem

Evolutionary fuzzy c-means clustering algorithm

Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis

An In-depth Analysis of Fuzzy C-Means Clustering for Cellular Manufacturing