A Novel Fuzzy Clustering Method for Outlier Detection in Data Mining

In data mining, the conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. This paper introduces the limitations of conventional clustering methods through k-means and fuzzy c-means clustering and demonstrates the drawbacks of the algorithms in handling outlier points. In this paper, we propose a new fuzzy clustering method which is more efficient in handling outlier points than conventional fuzzy c-means algorithm. The new method excludes outlier points by giving them extremely small membership values in existing clusters while fuzzy c-means algorithm tends give them outsized membership values. The new algorithm also incorporates the positive aspects of k-means algorithm in calculating the new cluster centers in a more efficient approach than the c-means method.