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Efficiency of K-Means Clustering Algorithm in Mining Outliers from Large Data Sets Efficiency of outlier detection using several centroid initialization methods

This paper presents the performance of k-means clustering algorithm, depending upon various mean values input methods. Clustering plays a vital role in data mining. Its main job is to group the similar data together based on the characteristic they possess. The mean values are the centroids of the specified number of cluster groups. The centroids, though gets changed during the process of clustering, are calculated using several methods. Clustering algorithms can be applied for image analysis, pattern recognition, bio-informatics and in several other fields. The clustering algorithm consists to two stages with first stage forming the clusters-calculating centroid and the second stage determining the outliers. There are three methods for assigning the mean values in k-means clustering algorithm. The three mean value assignment methods are implemented, performance is analysed and comparison of every method is done. Outliers, the disadvantage of the process are used in the analyzation to determine the performance with various mean inputs and methods.