Experimental Analysis of Fuzzy Clustering Techniques for Outlier Detection

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Fuzzy clustering is an effective clustering approach that divides the dataset into fuzzy segments. Fuzzy clustering and outlier detection are two interconnected processes. Outlier detection is essential because it discloses hidden patterns and crucial information about a dataset and is beneficial in a wide range of applications like fraud detection, military surveillance, identifying computer network intrusions, image processing, insurance or health care, etc. Many fuzzy clustering techniques are robust to anomalies since they limit the outlier's impact on the cluster's centroid.In this paper, we have compared four fuzzy clustering techniques namely Fuzzy C-means (FCM), Noise Clustering (NC), Credibilistic Fuzzy C-means (CFCM), and Density Oriented Fuzzy C - Means (DOFCM) on the basis of some crucial properties that are essential for efficient outlier detection. To better evaluate the feasibility of all these techniques, we experimentally evaluated these techniques on six datasets (two Real and four Synthetic) in the presence of noise and outliers. The results of the comparative analysis show that DOFCM outperforms all other techniques in terms of outlier detection and cluster formation. This research will surely benefit any researcher willing to detect outliers in their study using fuzzy clustering or either implicitly or explicitly working on fuzzy clusters.