Abstract: in the paper new recurrent adaptive algorithm for fuzzy clustering of data with missing values is proposed. This algorithm is based on fuzzy probabilistic clustering procedures and self-learning Kohonen’s rule using principle “Winner-Takes-More” with Cauchy neighborhood function. Using proposed approach it’s possible to solve clustering task in on-line mode in situation when the amount of missing values in data is too big.

Keywords: fuzzy clustering, Kohonen self-organizing network, learning rule, incomplete data with missing values.

ACM Classification Keywords: 1.2.6 Artificial Intelligence: Learning – Connectionism and neural nets; 1.2.8 Artificial Intelligence: Problem Solving, Control Methods, and Search – Control theory; 1.5.1 Pattern Recognition: Clustering – Algorithms.

Link:

ADAPTIVE FUZZY PROBABILISTIC CLUSTERING OF INCOMPLETE DATA

Yevgeniy Bodyanskiy, Alina Shafronenko, Valentyna Volkova

http://www.foibg.com/ijima/vol02/ijima02-02-p02.pdf