ADAPTIVE FUZZY PROBABILISTIC CLUSTERING OF INCOMPLETE DATA
Yevgeniy V. Bodyanskiy, Аліна Шафроненко, Valentyna Volkova · 2013
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.