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Article: On cluster tree for nested and multi-density data clustering

TitleOn cluster tree for nested and multi-density data clustering
Authors
KeywordsMulti-densities
Cluster tree
Hierarchical clustering
K-Means-type algorithm
Issue Date2010
Citation
Pattern Recognition, 2010, v. 43, n. 9, p. 3130-3143 How to Cite?
AbstractClustering is one of the important data mining tasks. Nested clusters or clusters of multi-density are very prevalent in data sets. In this paper, we develop a hierarchical clustering approach-a cluster tree to determine such cluster structure and understand hidden information present in data sets of nested clusters or clusters of multi-density. We embed the agglomerative k-means algorithm in the generation of cluster tree to detect such clusters. Experimental results on both synthetic data sets and real data sets are presented to illustrate the effectiveness of the proposed method. Compared with some existing clustering algorithms (DBSCAN, X-means, BIRCH, CURE, NBC, OPTICS, Neural Gas, Tree-SOM, EnDBSAN and LDBSCAN), our proposed cluster tree approach performs better than these methods. © 2010 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/276880
ISSN
2017 Impact Factor: 3.965
2015 SCImago Journal Rankings: 2.051

 

DC FieldValueLanguage
dc.contributor.authorLi, Xutao-
dc.contributor.authorYe, Yunming-
dc.contributor.authorLi, Mark Junjie-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:34:56Z-
dc.date.available2019-09-18T08:34:56Z-
dc.date.issued2010-
dc.identifier.citationPattern Recognition, 2010, v. 43, n. 9, p. 3130-3143-
dc.identifier.issn0031-3203-
dc.identifier.urihttp://hdl.handle.net/10722/276880-
dc.description.abstractClustering is one of the important data mining tasks. Nested clusters or clusters of multi-density are very prevalent in data sets. In this paper, we develop a hierarchical clustering approach-a cluster tree to determine such cluster structure and understand hidden information present in data sets of nested clusters or clusters of multi-density. We embed the agglomerative k-means algorithm in the generation of cluster tree to detect such clusters. Experimental results on both synthetic data sets and real data sets are presented to illustrate the effectiveness of the proposed method. Compared with some existing clustering algorithms (DBSCAN, X-means, BIRCH, CURE, NBC, OPTICS, Neural Gas, Tree-SOM, EnDBSAN and LDBSCAN), our proposed cluster tree approach performs better than these methods. © 2010 Elsevier Ltd. All rights reserved.-
dc.languageeng-
dc.relation.ispartofPattern Recognition-
dc.subjectMulti-densities-
dc.subjectCluster tree-
dc.subjectHierarchical clustering-
dc.subjectK-Means-type algorithm-
dc.titleOn cluster tree for nested and multi-density data clustering-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2010.03.020-
dc.identifier.scopuseid_2-s2.0-78649574534-
dc.identifier.volume43-
dc.identifier.issue9-
dc.identifier.spage3130-
dc.identifier.epage3143-

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