An Additive Fuzzy Clustering Model

Developing Additive Spectral Approach to Fuzzy Clustering

An additive spectral method for fuzzy clustering is presented. The method operates on a clustering model which is an extension of the spectral decomposition of a square matrix. The computation proceeds by extracting clusters one by one, which allows us to draw several stopping rules to the procedure. We experimentally test the performance of our method and show its competitiveness. In spite of ...

An Optimization Model for Fuzzy Binary Clustering

Clustering has been a powerful tool to visualize complex data with extensive applications in many disciplines. In this paper, we propose an optimization-based solution to the fuzzy binary clustering problem by grouping all the data points into two clusters. Our model are based on two assumptions. One is that the similar objects are labeled similarly, which is known as the “cluster assumption” i...

Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering

Let Q be a given n× n square symmetric matrix of nonnegative elements between 0 and 1, e.g. similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters. In additive fuzzy clustering, the n × K fuzzy memberships matrix P is found by leastsquares approximation of the off-diagonal elements of Q by inner products of rows of P. By contrast, kernelized fuzzy c-means is not...

An Algorithm to Model Paradigm Shifting in Fuzzy Clustering

The graded possibilistic clustering paradigm includes as the two extreme cases the “probabilistic” assumption and the “possibilistic” assumption adopted by many clustering algorithms. We propose an implementation of a graded possibilistic clustering algorithm based on an interval equality constraint enforcing both the normality condition and the required graded possibilistic condition. Experime...

AN INTEGRATED ADAPTIVE FUZZY CLUSTERING MODEL FOR PATTERN RECOGNITION by