Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract

An integration of clustering algorithm was proposed for the shortage of the DBSCAN algorithm in inhomogeneous distribution and large-scale data processing.The algorithm extracted representative data from the original data set using information entropy and ant colony clustering technology,and did DBS...

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Main Authors: Yong-hua ZHANG, Fei-ming DU, Dai-wen WU
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2012-11-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2012.z2.042/
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author Yong-hua ZHANG
Fei-ming DU
Dai-wen WU
author_facet Yong-hua ZHANG
Fei-ming DU
Dai-wen WU
author_sort Yong-hua ZHANG
collection DOAJ
description An integration of clustering algorithm was proposed for the shortage of the DBSCAN algorithm in inhomogeneous distribution and large-scale data processing.The algorithm extracted representative data from the original data set using information entropy and ant colony clustering technology,and did DBSCAN clustering based on the representative data subset.The experiment show that this algorithm is effective to reduce the I/O-consuming and memory requirements,and resolve the cluster problem of large-scale data sets containing categories property.
format Article
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institution Kabale University
issn 1000-436X
language zho
publishDate 2012-11-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-a2d8897acef243b580c47b2ef2bf0f382025-01-14T06:34:41ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2012-11-013329029359670442Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstractYong-hua ZHANGFei-ming DUDai-wen WUAn integration of clustering algorithm was proposed for the shortage of the DBSCAN algorithm in inhomogeneous distribution and large-scale data processing.The algorithm extracted representative data from the original data set using information entropy and ant colony clustering technology,and did DBSCAN clustering based on the representative data subset.The experiment show that this algorithm is effective to reduce the I/O-consuming and memory requirements,and resolve the cluster problem of large-scale data sets containing categories property.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2012.z2.042/information entropyclusteringDBSCANant colony optimization
spellingShingle Yong-hua ZHANG
Fei-ming DU
Dai-wen WU
Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
Tongxin xuebao
information entropy
clustering
DBSCAN
ant colony optimization
title Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
title_full Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
title_fullStr Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
title_full_unstemmed Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
title_short Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract
title_sort improved density dased clustering algorithm based on information entropy and ant colony optimization abstract
topic information entropy
clustering
DBSCAN
ant colony optimization
url http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2012.z2.042/
work_keys_str_mv AT yonghuazhang improveddensitydasedclusteringalgorithmbasedoninformationentropyandantcolonyoptimizationabstract
AT feimingdu improveddensitydasedclusteringalgorithmbasedoninformationentropyandantcolonyoptimizationabstract
AT daiwenwu improveddensitydasedclusteringalgorithmbasedoninformationentropyandantcolonyoptimizationabstract