Fuzzy Clustering for Detecting Linear Structures with Different Dimensions
Kazutaka Umayahara, Yoshiteru Nakamori, Sadaaki Miyamoto · Journal of Advanced Computational Intelligence and Intelligent Informatics · 1999
One recent interest in fuzzy clustering is the simultaneous determination of a fuzzy partition of a given dataset and parameters of assumed models having different shapes that explain partitioned datasets. We propose an objective function to detect linear varieties with different dimensionalities. The noise cluster suggested by Dave is introduced. Since this is not all-purpose method, some techniques are suggested using artificial examples to show how to implement clustering successfully.