Fuzzy clustering-based approach for outlier detection
Moh’d Belal Al-Zoubi, Al-Dahoud Ali, Abdelfatah A. Yahya · 2010
Outlier detection is an important task in a wide variety of application areas. In this paper, a proposed method based on fuzzy clustering approaches for outlier detection is presented. We first perform the c-means fuzzy clustering algorithm. Small clusters are then determined and considered as outlier clusters. The rest of outliers (if any) are then detected in the remaining clusters based on temporary removing a point from the data set and recalculating the objective function. If a noticeable change occurred in the Objective Function (OF), the point is considered an outlier. Experimental results show that our method works well. The test results show that the proposed approach gave good results when applied to different data sets.