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Clustering is often viewed as a synonym of techniques used to reveal the structure in data. The inherent geometrical diversity of data is a strong motivating factor to search for geometrically flexible clusters design supported by the clustering algorithms. In this study, we introduce a concept of geometrically variable fuzzy clustering (making use of Fuzzy C-Means, FCM), in which the fuzzification...
A clustering method, called HACO (Hyperbox clustering with Ant Colony Optimization), is proposed for classifying unlabeled data using hyperboxes and an ant colony meta-heuristic. It acknowledges the topological information (inherently associated to classification) of the data while looking in a small search space, providing results with high precision in a short time. It is validated using artificial...
This study is concerned with a concept of structural expansion of fuzzy relations. Roughly speaking, when dealing with a finite family of highly dimensional fuzzy relations Ri, i = 1,2,...c, the intent is to provide a way of expressing them as Cartesian products of some fuzzy sets and ldquoreducedrdquo fuzzy relations of lower dimensionality. More descriptively, this expansion is regarded as a vehicle...
Structural relationships in data are revealed by methods of clustering and fuzzy clustering. In essence, clustering leads to the reduction of data. Dimensionality reduction comes as a complementary process in which we eliminate some features (attributes). This study introduces a concept of structure reduction which is guided by a criterion of structure retention. In particular, it is shown that each...
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