Adaptive neuro-fuzzy clustering of distorted data based on prototype-centroid strategy using evolutionary procedures

Abstract

The problem of clustering is a very relevant area in Data Mining of different nature. To solve this problem, there are a large number of known methods and algorithms, most of which work in batch mode, in conditions when the entire of data set is known in advance and does not change over the time. These methods are complex in software implementa-tion and are not without drawbacks. The aim of the work is to develop an adaptive neuro-fuzzy clustering method of distorted data based on proto-type-centroid strategy using evolutionary procedures, that solves the problem in online mode, when data are fed se-quentially in real time and is characterized by numerical simplicity and high speed. The problem of adaptive fuzzy clustering of distorted data by outliers and emissions, which are presented in the form of vector arrays, based on the strategy of the nearest prototype - centroid using evolutionary procedures, is con-sidered. The proposed approach is based on the online probabilistic fuzzy clustering procedure with the membership function of special type and the evolutionary cat swarm algorithm. Proposed adaptive neuro-fuzzy clustering method of distorted data based on prototype-centroid strategy using evolutionary procedures characterized by computational simplicity, high speed and accuracy of the results based on experimental studies. The modification of optimization procedure that based on cat swarm algorithm was propose. The proposed method is simple in numerical implementation, workable in the case when the data is distorted and are fed sequentially in online mode, that is confirmed experimentally.