Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence.
Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data...
| Publicado en: | Scientific World Journal Vol. 2015; pp. 1 - 6 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
10/1/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110311827&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110311827 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 10/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 110311827 110311827 NLM26495413 10.1155/2015/107650 NLM26495413 PMC4606166 110311827 ppf: 1 ppct: 5 formats: tig: atl: Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence. aug: au: Srinivasan, Thenmozhi Palanisamy, Balasubramanie affil: Department of Computer Applications, Gnanamani College of Technology, AK Samuthiram, Pachal, Namakkal District, Tamil Nadu 637 018, India sug: ab: Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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