Перегляд за автором "Shafronenko, A. Yu."
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Публікація Adaptive Neuro-Fuzzy Methods for Distorted Data Clustering(2020) Shafronenko, A. Yu.; Bodyanskiy, Ye. V.; Rudenko, D. A.The problem of data sets described by vector-images clustering often occurs in many applications associated with Data Mining [40, 41], when processed vector-image with different levels of probabilities, possibilities or memberships, can belong to more than one class. However, there are situations when the data sets contain missing values. In this situation more effective is to use mathematical apparatus of Computational Intelligence [Rutkowski, 2008] and, first of all artificial neural networks [8], that solve task of restoring the lost observations and modifications of the popular method of fuzzy c-means [22], which solve the problem of clustering without recovery of data. Existing approaches for data processing with missing values [6], are efficient in cases when the massive of the original observations is given in batch form and does not change during the processing. At the same time, there is a wide class of problems in which the data that arrive to the processing, have the form of sequence that is feed in real time as it occurs in the training of Kohonen self-organizing maps [1] or their modifications [2]. In this regard we have introduced [42] the adaptive neuro-fuzzy Kohonen network to solve the problem of clustering data with gaps based on the strategy of partial distances (PDS FCM). However, in situations where the number of such missing values is too big, the strategy of partial distances may be not effective, and therefore it may be necessary, along with the solution of fuzzy clustering simultaneously estimate the missing observations. In this situation, a more efficient is approach that is based on the optimal expansion strategy (OCS FCM) [22]. This chapter is devoted to the task of on-line data clustering using the optimal expansion strategy, adapted to the case when information is processed in a sequential mode, and its volume is not determined in advance.Публікація Evolutionary Algorithms Based on Cat Swarm Optomozation in Fuzzy Clustering Tasks(2020) Shafronenko, A. Yu.; Bodyanskiy, Ye. V.; Rudenko, D. A.Computational intelligence methods are widely used to solve many complex problems, both purely scientific, and in the field of engineering, business, finance, medical and technical diagnostics, and other areas related to information processing, including, of course, traditional intellectual analysis: Data Mining and such new directions as Dynamic Data Mining, Data Stream Mining, Big Data Mining, Web Mining, Text Mining, etc. [1-6]. One of the main areas of computational intelligence is evolutionary algorithms, which are essentially certain mathematical models of reproduction or development of biological organisms, inspired by nature and intended, in the most general case, to find the global optimum of multi-extremal functions under uncertainty. Historically, the first evolutionary algorithms were the so-called genetic algorithms, which are based on the selection and genetics mechanisms that implement the survival of the strongest individuals in the process of evolution. The most popular evolutionary bioinspired algorithms for today are the so-called “swarm” procedures (Particle Swarm Optimization - PSO) [7], among which, as for the performance and simplicity of implementation is Cat Swarm Optimization (CSO) [8,9]. These algorithms have confirmed their effectiveness in solving a number of rather complex tasks and have already “managed” to undergo a number of modifications, among which are procedures based on harmonic search, fractional derivatives, adaptation of search parameters and, finally, “crazy cats” [ 10-16]. At the same time, these procedures are not deprived of some shortcomings that degrade the properties of the global extremum search process. The aim of the chapter is to develop a fast-acting and numerically simple method of evolutionary optimization under conditions of multi-extremal goal functions.Публікація On-line robust fuzzy clustering based on similarity measure(2013) Bodyanskiy, Ye. V.; Shafronenko, A. Yu.The problem offuzzy adaptive on-line clustering of data distorted by outliers sequentially supplied to the processing when the original sample volume and the number of distorted observations are unknown is considered. The probabilistic and possibilistic clustering algorithms for such data, that are based on the similarity measure of a special kind that weakens or overwhelming outliers are proposed.Публікація Online algorithm for possibilitic fuzzy clustering based on evolutionary cat swarm optimization(2019) Bodyanskiy, Ye. V.; Shafronenko, A. Yu.The problem of clustering of multidimensional observations is often found in many applications related to data mining and exploratory data analysis. The traditional approach to solving these problems requires that every observation could belong to only one cluster at a more natural is situations when a feature vector with the various possible levels of memberships can belong to multiple classes. This situation is the subject of fuzzy cluster analysis, rapidly developing now. We propose online adaptive approach for this task solving.Публікація Online Recurrent Me-thod Of Credibilistic Fuzzy Clusterin(2020) Bodyanskiy, Ye. V.; Shafronenko, A. Yu.; Rudenko, D. A.; Klymova, I. N.An online method of reliable fuzzy clustering is proposed, designed to analyze data sequentially received for processing. A feature of the developed approach is the use of the membership function of a special kind described by the density function of the Cauchy distribution. The actual procedure for clarifying the centroids of clusters is essentially a self-learning rule “The Winner Takes More” (WTM), in which the neighborhood function is generated by the introduced membership function.Публікація Online Сredibilistic Fuzzy Clustering of Data with Gaps(2020) Bodyanskiy, Ye. V.; Shafronenko, A. Yu.; Klymova, I. N.The problem of online fuzzy clustering based on credibilistic approaches based on the partial distance strategy and processing of data with gaps was proposed. The considered methods are simple in numerical implementation, being essentially gradient procedures for optimizing objective functions of a special type.