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dc.contributor.authorRen, Yan
dc.contributor.authorXiaodong, Liu
dc.contributor.authorLiu, Wan-Quan
dc.date.accessioned2017-01-30T13:36:38Z
dc.date.available2017-01-30T13:36:38Z
dc.date.created2013-03-24T20:00:31Z
dc.date.issued2012
dc.identifier.citationRen, Yan and Xiaodong, Liu and Liu, Wan-Quan. 2012. DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric. Applied Soft Computing. 12 (5): pp. 1542-1554.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/33348
dc.identifier.doi10.1016/j.asoc.2011.12.015
dc.description.abstract

In this paper we propose a new density based clustering algorithm via using the Mahalanobis metric. This is motivated by the current state-of-the-art density clustering algorithm DBSCAN and some fuzzy clustering algorithms. There are two novelties for the proposed algorithm: One is to adopt the Mahalanobis metric as distance measurement instead of the Euclidean distance in DBSCAN and the other is its effective merging approach for leaders and followers defined in this paper. This Mahalanobis metric is closely associated with dataset distribution. In order to overcome the unique density issue in DBSCAN, we propose an approach to merge the sub-clusters by using the local sub-cluster density information. Eventually we show how to automatically and efficiently extract not only ‘traditional’ clustering information, such as representative points, but also the intrinsic clustering structure. Extensive experiments on some synthetic datasets show the validity of the proposed algorithm. Further the segmentation results on some typical images by using the proposed algorithm and DBSCAN are presented in this paper and they are shown that the proposed algorithm can produce much better visual results in image segmentation.

dc.publisherElsevier
dc.subjectMahalanobis distance
dc.subjectFollowers
dc.subjectImage segmentation
dc.subjectClustering
dc.subjectLeaders
dc.titleDBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
dc.typeJournal Article
dcterms.source.volume12
dcterms.source.number5
dcterms.source.startPage1542
dcterms.source.endPage1554
dcterms.source.issn15684946
dcterms.source.titleApplied Soft Computing
curtin.department
curtin.accessStatusFulltext not available


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