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In this study, we discuss a design of fuzzy associative structures (memories) realized within the framework of fuzzy clustering. Associative memories are inherently direction-free structures (and the recall of objects can be realized for any variable or a subset of variables). Fuzzy clustering being direction-free comes here as a sound design alternative. Two recall proposals are studied: one involves prototypes (being the key descriptors of the structure of the data) and their activation in the presence of partially available data to be recalled whereas the second proposal involves fuzzy correlation matrices and in principle exhibits some resemblance with a standard correlation associative memories. In the setting of associative memories, Fuzzy C-Means (FCM) is studied. The recall error is discussed with regard to the essential parameters of the FCM (the number of clusters and the fuzzification coefficient). Furthermore we discuss an optimization of the distance function used in the clustering algorithm realized with regard to the recall error. Experimental results are provided along with a comparative study involving correlation-based associative memories. [ABSTRACT FROM AUTHOR]

Abstract: In real-world problems we encounter situations where patterns are described by blocks (families) of features where each of these groups comes with a well-expressed semantics. For instance, in spatiotemporal data we are dealing with spatial coordinates of the objects (say, x–y coordinates) while the temporal part of the objects forms another collection of features. It is apparent that when clustering objects being described by families of features, it becomes intuitively justifiable to anticipate their different role and contribution to the clustering process of the data whereas the clustering is sought to be reflective of an overall structure in the data set. To address this issue, we introduce an agreement based fuzzy clustering—a fuzzy clustering with blocks of features. The detailed investigations are carried out for the well-known algorithm of fuzzy clustering that is fuzzy C-means (FCM). We propose an extended version of the FCM where a composite distance function is endowed with adjustable weights (parameters) quantifying an impact coming from the blocks of features. A global evaluation criterion is used to assess the quality of the obtained results. It is treated as a fitness function in the optimization of the weights through the use of particle swarm optimization (PSO). The behavior of the proposed method is investigated in application to synthetic and real-world data as well as a certain case study. [Copyright &y& Elsevier]

This paper is concerned with a new architecture of a reinforced hybrid interval fuzzy neural networks (RHIFNN) classifier developed with aid of Fuzzy C-Means (FCM) clustering and Particle Swarm Optimization (PSO). The key objectives of this study concern the following: (a) selection of preprocessing techniques for the dimensionality reduction of input space. Linear discriminant analysis (LDA) or principal component analysis (PCA) algorithm forms a front end of the network to form the low-dimensional input variables. (b) The efficient process of dealing with uncertain information by interval type-2 fuzzy sets using Fuzzy C-Means (FCM) clustering. The premise (condition) part of the rules is realized by two FCM clustering algorithms, which are invoked by using different values of the fuzzification coefficient subsequently resulting in interval-valued type-2 membership functions. (c) The simultaneous structural and parametric optimization of network by evolutionary algorithm is completed. The parameters of the network including both the premise and consequent parts are optimized by means of the particle swarm optimization (PSO). The proposed classifier is applied to a variety of machine learning datasets and the results are compared with those provided by other classifiers reported in the literature. [ABSTRACT FROM AUTHOR]