New outlier detection method based on fuzzy clustering
Moh’d Belal Al-Zoubi, Ali A. Al-Dahoud, Abdelfatah A. Yahya · 2010
In this paper, a new efficient method for outlier detection is proposed. The proposed method is based on fuzzy clustering techniques. The c-means algorithm is first performed, then small clusters are determined and considered as outlier clusters. Other outliers are then determined based on computing differences between objective function values when points are temporarily removed from the data set. If a noticeable change occurred on the objective function values, the points are considered outliers. Test results were performed on different well-known data sets in the data mining literature. The results showed that the proposed method gave good results.