Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm
This is an incremental improvement for data mining and image analysis fields, offering a modified clustering approach.
The authors tackled clustering by extending the Fuzzy C-Means algorithm to use multi-dimensional membership vectors per data point, resulting in a novel scheme tested on standard datasets and image segmentation with performance comparisons to the classical method.
Clustering is an important facet of explorative data mining and finds extensive use in several fields. In this paper, we propose an extension of the classical Fuzzy C-Means clustering algorithm. The proposed algorithm, abbreviated as VFC, adopts a multi-dimensional membership vector for each data point instead of the traditional, scalar membership value defined in the original algorithm. The membership vector for each point is obtained by considering each feature of that point separately and obtaining individual membership values for the same. We also propose an algorithm to efficiently allocate the initial cluster centers close to the actual centers, so as to facilitate rapid convergence. Further, we propose a scheme to achieve crisp clustering using the VFC algorithm. The proposed, novel clustering scheme has been tested on two standard data sets in order to analyze its performance. We also examine the efficacy of the proposed scheme by analyzing its performance on image segmentation examples and comparing it with the classical Fuzzy C-means clustering algorithm.