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Fuzzy classifiers have been studied in the area of fuzzy sets for a long time resulting in a number of architectures. In this study, we thoroughly investigate and critically assess fuzzy rule-based classifiers. A topology of the classifier is discussed along with a discussion of the role of fuzzy set technology in the construction of condition and conclusion parts of the classification rules. Some optimization mechanisms utilized in the adjustment of information granules forming the rules are presented. Performance of the fuzzy classifiers is quantified in terms of their accuracy and an area under curve ( AUC ) determined for the receiver operating characteristics ( ROC ). The performance of the classifier is evaluated vis-à-vis a collection of triangular norms used in the construction of the fuzzy classifiers. Experimental studies involve synthetic and publicly available data. Furthermore, comparative studies include the experiments with the commonly used non-fuzzy classifiers. [ABSTRACT FROM AUTHOR]

This study is concerned with clustering carried out in presence of labeled patterns. An objective of this optimization is to reconcile between the structure residing in data (and being primarily discovered by the underlying clustering mechanism) and the labels of the patterns forming such structure. In this sense, one can consider the supervised fuzzy clustering to be a framework of preliminary data analysis providing with a thorough insight into the structure of the data and supporting the ensuing design of detailed classifiers. The proposed method augments the standard fuzzy C-means algorithm by extending the original objective function by the supervision component (labeled patterns). Experimental results illustrate the approach and discuss the use of this type of clustering in vector quantization. [Copyright &y& Elsevier]

Abstract: In this article, a proximity fuzzy framework for clustering relational data is presented, where the relationships between the entities of the data are given in terms of proximity values. We offer a comprehensive and in-depth comparison of our clustering framework with proximity relational knowledge to clustering with distance relational knowledge, such as the well known relational Fuzzy C-Means (FCM). We conclude that proximity can provide a richer description of the relationships among the data and this offers a significant advantage when realizing clustering. We further motivate clustering relational proximity data and provide both synthetic and real-world experiments to demonstrate both the usefulness and advantage offered by clustering proximity data. Finally, a case study of relational clustering is introduced where we apply proximity fuzzy clustering to the problem of clustering a set of trees derived from software requirements engineering. The relationships between trees are based on the degree of closeness in both the location of the nodes in the trees and the semantics associated with the type of connections between the nodes. [Copyright &y& Elsevier]