Improving fuzzy C-means clustering algorithm based on a density-induced distance measure

The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed meth...

Full description

Saved in:
Bibliographic Details
Published in:Journal of engineering (Stevenage, England) England), 2014-04, Vol.2014 (4), p.137-139
Main Authors: Lu, Chunhong, Xiao, Shaoqing, Gu, Xiaofeng
Format: Article
Language:English
Subjects:
density-induced distance measure
fuzzy C-means clustering algorithm
fuzzy set theory
pattern clustering
real data sets
relative density degree
synthetic data sets
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3
cites cdi_FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3
container_end_page 139
container_issue 4
container_start_page 137
container_title Journal of engineering (Stevenage, England)
container_volume 2014
creator Lu, Chunhong
Xiao, Shaoqing
Gu, Xiaofeng
description The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.
doi_str_mv 10.1049/joe.2014.0053
format article
fullrecord <record><control><sourceid>wiley_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1049_joe_2014_0053</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_c300fe510e514ef1a311a0ee5711488d</doaj_id><sourcerecordid>TJE2BF00046</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3</originalsourceid><addsrcrecordid>eNp9kMFLwzAUh4soKOrRey8ePHS-NGm3HXVMnQi7TPAWXl9eZkbXjqRV5l9v5kQ8iIeQ8PL9fglfklwIGAhQ4-tVy4MchBoAFPIgOcmhEJmUUBz-Oh8n5yGsAEBIlYMSJ8nLbL3x7ZtrlqntPz626SRbMzYhpboPHfvdBdbL1rvudZ1WGNikbZNiargJrttmrjE9xaFxocOGOI3x0Hs-S44s1oHPv_fT5Pluupg8ZE_z-9nk5ikjqVSZDa0pjIUSRWXkeKhkOS6ZlMwJmBVZFKPK2JIqMgbGZEqkqgLKcUSEuUJ5msz2vabFld54t0a_1S06_TVo_VKj7xzVrEkCWC4ExKXYCpRCYHymGAqhRiMTu7J9F_k2BM_2p0-A3lnW0bLeWdY7y5Ev9_y7q3n7P6wXj9P89i66V2UMXu6DjruI9b6JivTjfPqL3xgbuas_uL8_8wkdNpth</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Improving fuzzy C-means clustering algorithm based on a density-induced distance measure</title><source>IET Digital Library - eJournals</source><source>Wiley Online Library Open Access</source><creator>Lu, Chunhong ; Xiao, Shaoqing ; Gu, Xiaofeng</creator><creatorcontrib>Lu, Chunhong ; Xiao, Shaoqing ; Gu, Xiaofeng</creatorcontrib><description>The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.</description><identifier>ISSN: 2051-3305</identifier><identifier>EISSN: 2051-3305</identifier><identifier>DOI: 10.1049/joe.2014.0053</identifier><language>eng</language><publisher>The Institution of Engineering and Technology</publisher><subject>density-induced distance measure ; fuzzy C-means clustering algorithm ; fuzzy set theory ; pattern clustering ; real data sets ; relative density degree ; synthetic data sets</subject><ispartof>Journal of engineering (Stevenage, England), 2014-04, Vol.2014 (4), p.137-139</ispartof><rights>2021 The Institution of Engineering and Technology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3</citedby><cites>FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fjoe.2014.0053$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fjoe.2014.0053$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,9735,11542,27903,27904,46030,46454</link.rule.ids></links><search><creatorcontrib>Lu, Chunhong</creatorcontrib><creatorcontrib>Xiao, Shaoqing</creatorcontrib><creatorcontrib>Gu, Xiaofeng</creatorcontrib><title>Improving fuzzy C-means clustering algorithm based on a density-induced distance measure</title><title>Journal of engineering (Stevenage, England)</title><description>The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.</description><subject>density-induced distance measure</subject><subject>fuzzy C-means clustering algorithm</subject><subject>fuzzy set theory</subject><subject>pattern clustering</subject><subject>real data sets</subject><subject>relative density degree</subject><subject>synthetic data sets</subject><issn>2051-3305</issn><issn>2051-3305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>DOA</sourceid><recordid>eNp9kMFLwzAUh4soKOrRey8ePHS-NGm3HXVMnQi7TPAWXl9eZkbXjqRV5l9v5kQ8iIeQ8PL9fglfklwIGAhQ4-tVy4MchBoAFPIgOcmhEJmUUBz-Oh8n5yGsAEBIlYMSJ8nLbL3x7ZtrlqntPz626SRbMzYhpboPHfvdBdbL1rvudZ1WGNikbZNiargJrttmrjE9xaFxocOGOI3x0Hs-S44s1oHPv_fT5Pluupg8ZE_z-9nk5ikjqVSZDa0pjIUSRWXkeKhkOS6ZlMwJmBVZFKPK2JIqMgbGZEqkqgLKcUSEuUJ5msz2vabFld54t0a_1S06_TVo_VKj7xzVrEkCWC4ExKXYCpRCYHymGAqhRiMTu7J9F_k2BM_2p0-A3lnW0bLeWdY7y5Ev9_y7q3n7P6wXj9P89i66V2UMXu6DjruI9b6JivTjfPqL3xgbuas_uL8_8wkdNpth</recordid><startdate>201404</startdate><enddate>201404</enddate><creator>Lu, Chunhong</creator><creator>Xiao, Shaoqing</creator><creator>Gu, Xiaofeng</creator><general>The Institution of Engineering and Technology</general><general>Wiley</general><scope>IDLOA</scope><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>201404</creationdate><title>Improving fuzzy C-means clustering algorithm based on a density-induced distance measure</title><author>Lu, Chunhong ; Xiao, Shaoqing ; Gu, Xiaofeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>density-induced distance measure</topic><topic>fuzzy C-means clustering algorithm</topic><topic>fuzzy set theory</topic><topic>pattern clustering</topic><topic>real data sets</topic><topic>relative density degree</topic><topic>synthetic data sets</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lu, Chunhong</creatorcontrib><creatorcontrib>Xiao, Shaoqing</creatorcontrib><creatorcontrib>Gu, Xiaofeng</creatorcontrib><collection>IET Digital Library (Open Access)</collection><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of engineering (Stevenage, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lu, Chunhong</au><au>Xiao, Shaoqing</au><au>Gu, Xiaofeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving fuzzy C-means clustering algorithm based on a density-induced distance measure</atitle><jtitle>Journal of engineering (Stevenage, England)</jtitle><date>2014-04</date><risdate>2014</risdate><volume>2014</volume><issue>4</issue><spage>137</spage><epage>139</epage><pages>137-139</pages><issn>2051-3305</issn><eissn>2051-3305</eissn><abstract>The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/joe.2014.0053</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2051-3305
ispartof Journal of engineering (Stevenage, England), 2014-04, Vol.2014 (4), p.137-139
issn 2051-3305
2051-3305
language eng
recordid cdi_crossref_primary_10_1049_joe_2014_0053
source IET Digital Library - eJournals; Wiley Online Library Open Access
subjects density-induced distance measure
fuzzy C-means clustering algorithm
fuzzy set theory
pattern clustering
real data sets
relative density degree
synthetic data sets
title Improving fuzzy C-means clustering algorithm based on a density-induced distance measure
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T19%3A39%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-wiley_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Improving%20fuzzy%20C-means%20clustering%20algorithm%20based%20on%20a%20density-induced%20distance%20measure&rft.jtitle=Journal%20of%20engineering%20(Stevenage,%20England)&rft.au=Lu,%20Chunhong&rft.date=2014-04&rft.volume=2014&rft.issue=4&rft.spage=137&rft.epage=139&rft.pages=137-139&rft.issn=2051-3305&rft.eissn=2051-3305&rft_id=info:doi/10.1049/joe.2014.0053&rft_dat=%3Cwiley_cross%3ETJE2BF00046%3C/wiley_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3446-7fd5df06a1bd39743696ec432c0ee4cfa18bdf6cbcdd09cd6acbb0c2a8cca24a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true