Fuzzy Clustering and Fuzzy Clustering Models

OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...

Fuzzy clustering models and applications

Fuzzy local regression models with fuzzy clustering

The TSK model introduced by Takagi Sugeno and Kang TSK fuzzy reasoning is associated with fuzzy rules that have a special format with a func tional type consequent instead of the fuzzy consequent that normally appears in the MamdamiModel In this way the TSK approach tries to decompose the input space into subspaces and then approximate the system in each subspace by a simple linear regression m...

Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression

Obtaining Interpretable Fuzzy Models from Fuzzy Clustering and Fuzzy Regression* Frank Hiippner Frank Klawonn University of Applied Sciences, Emden Department of Electrical Engineering and Computer Science Constantiaplatz 4 D-26723 Emden, Germany e-mail alias: [email protected] In this paper we develop an objective finctionbased clustering algorithm to build fizzy models of the Takagi-Sug...

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...