Integration of artificial immune network and K-means for cluster analysis

• Published in: Knowledge and information systems Vol. 40; no. 3; pp. 541 - 557
• Main Authors: Kuo, R. J., Chen, S. S., Cheng, W. C., Tsai, C. Y.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2014; Springer; Springer Nature B.V
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Antibodies; Applied sciences; Artificial intelligence; Benchmarking; Breast cancer; Cancer; Cloning; Cluster analysis; Clustering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Datasets; Exact sciences and technology; Experiments; Fault diagnosis; Immune system; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Learning and adaptive systems; Memory organisation. Data processing; Methods; Networks; Optimization; Optimization algorithms; Pattern recognition. Digital image processing. Computational geometry; Regular Paper; Simulation; Software; Studies; Theoretical computing; Cluster analysis; K-means algorithm; Artificial immune system; Particle swarm optimization algorithm; Artificial immune network; Computer vision; Artificial life; Data analysis; K means algorithm; Particle swarm optimization; Classification; Swarm intelligence; Artificial intelligence; Algorithm analysis; Immune system
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-013-0649-3

This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer, are employed to testify the proposed algorithm. The computational results reveal that aiNetK is superior to particle swam optimization and artificial immune system-related methods.