An improved method of fuzzy c-means clustering by using feature selection and weighting

Fuzzy C-means has been utilized successfully in a wide range of applications, extending from the clustering capability of the K-means to datasets that are uncertain, vague and otherwise are hard to be clustered. In cluster analysis, certain features of a given data set may exhibit higher relevance in comparison to others. To address this issue, Feature-Weighted Fuzzy C-Means approaches have emerged in recent years. However, there are certain deficiencies in the existing methods, e.g., the elements in a feature-weight vector cannot be adaptively adjusted during the training phase, and the update formulas of a feature-weight vector cannot be derived analytically. In this study, an Improved Feature-Weighted Fuzzy C-Means is proposed to overcome to these shortcomings. A novel initialization scheme for the fuzzy c-means algorithm was proposed. Finally, the proposed method was applied into data clustering. The experimental results showed that the proposed method can be considered as a promising tool for data clustering.