On cluster tree for nested and multi-density data clustering

Clustering 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 o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Pattern recognition Jg. 43; H. 9; S. 3130 - 3143
Hauptverfasser: Li, Xutao, Ye, Yunming, Li, Mark Junjie, Ng, Michael K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.09.2010
Elsevier
Schlagworte:
Agglomeration
Algorithms
Applied sciences
Artificial intelligence
Cluster tree
Clustering
Clusters
Computer science; control theory; systems
Connectionism. Neural networks
Cures
Exact sciences and technology
Hierarchical clustering
Information theory
Information, signal and communications theory
k-Means-type algorithm
Multi-densities
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Tasks
Telecommunications and information theory
Trees
Multi-densities
Cluster tree
k-Means-type algorithm
Hierarchical clustering
Performance evaluation
Automatic classification
Hierarchical classification
Data processing
K means algorithm
Neural network
Data mining
Signal classification
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Clustering 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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2010.03.020