Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 115, Issue -, Pages 13-31Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2019.09.004
Keywords
Nonlinearly separable data; Density-based clustering; Kernel-based clustering; Graph-based clustering; Manifold-based clustering; Fuzzy approach to clustering
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In many practical situations data may be characterized by nonlinearly separable clusters. Classical (hard or fuzzy) clustering algorithms produce a partition of objects by computing the Euclidean distance. As such, they are based on the linearity assumption and, therefore, do not identify properly clusters characterized by nonlinear structures. To overcome this limitation, several approaches can be followed: density-, kernel-, graph- or manifold-based clustering. A review of these approaches is offered and some new fuzzy manifold-based clustering algorithms, involving the so-called geodesic distance, are proposed. The effectiveness of such algorithms is shown by synthetic. benchmark and real data. (C) 2019 Elsevier Inc. All rights reserved.
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