A local geometrical properties application to fuzzy clustering

✍ Scribed by Antonio Flores-Sintas; JoséM. Cadenas; Fernando Martin

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 779 KB
• Volume: 100
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(97)00038-9

✦ Synopsis

Possibilistic clustering is seen increasingly as a suitable means to resolve the limitations resulting from the constraints imposed in the fuzzy C-means algorithm. Studying the metric derived from the covariance matrix we obtain a membership function and an objective function whether the Mahalanobis distance or the Euclidean distance is used. Applying the theoretical results using the Euclidean distance we obtain a new algorithm called fuzzy-minimals, which detects the possible prototypes of the groups of a sample. We illustrate the new algorithm with several examples.