A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

In this paper, a new symmetry-based genetic clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 20; číslo 11; s. 1441 - 1457
Hlavní autori:	Bandyopadhyay, S., Saha, S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.11.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Clustering Clustering algorithms Clusters Computer science; control theory; systems Convergence Data mining Data processing. List processing. Character string processing Encoding Euclidean distance Evolutionary computing and genetic algorithms Exact sciences and technology Genetic algorithms Genetics Memory organisation. Data processing Nearest neighbor searches Particle measurements Partitioning Partitioning algorithms Pattern Recognition Shape measurement Similarity measures Software Strings Studies Symmetry Validity Evolutionary computing and genetic algorithms Pattern Recognition Algorithms Similarity measures Cluster analysis Nearest neighbour Artificial life Euclidean theory Data partition K means algorithm Genetic distance real encoding Unsupervised classification genetic algorithms Data mining Computational complexity Unsupervised learning Symmetry group point symmetry-based distance Genetic algorithm Character string symmetry Weight function cluster validity index Kd-tree Non convex analysis
ISSN:	1041-4347, 1558-2191
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Popis
Shrnutí:	In this paper, a new symmetry-based genetic clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here, assignment of points to different clusters are done based on a point symmetry (PS)-based distance rather than the Euclidean distance. A newly proposed PS-based cluster validity index, sym-index, is used as a measure of the validity of the corresponding partitioning. The algorithm is, therefore, able to detect both convex and nonconvex clusters irrespective of their sizes and shapes as long as they possess the symmetry property. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing PS-based distance. A proof on the convergence property of variable string length genetic algorithm with PS- distance-based clustering (VGAPS-clustering) technique is also provided. The effectiveness of VGAPS-clustering compared to variable string length genetic K-means algorithm (GCUK-clustering) and one recently developed weighted sum validity function-based hybrid niching genetic algorithm (HNGA-clustering) is demonstrated for nine artificial and five real-life data sets.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1041-4347 1558-2191
DOI:	10.1109/TKDE.2008.79