Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system

Applied Soft Computing Journal

Volumn 11, Issue 2, 2011, Pages 2698-2713

  Graaff, A J ^a   Engelbrecht, A P ^a  

^a UNIVERSITY OF PRETORIA   (South Africa)

Author keywords

Clustering performance measures; dynamic clustering; Immune networks; Sequential deviation detection

Indexed keywords

ARTIFICIAL IMMUNE SYSTEM; DATA CLUSTERING; DATA SETS; DYNAMIC CLUSTERING; IMMUNE NETWORKS; LOCAL NETWORKS; NEIGHBOURHOOD; NETWORK THEORY; NUMBER OF CLUSTERS; PERFORMANCE MEASURE; SEQUENTIAL DEVIATION DETECTION; TECHNIQUES USED;

CLUSTERING ALGORITHMS;

IMMUNOLOGY;

EID: 78751616383     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2010.10.017     Document Type: Conference Paper

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