Enhanced K-Means with Greedy Algorithm for Outlier Detection

Due to significant development in information technology, larger and huge volumes of data are accumulated in databases. In order to make the most out of this huge collection, well-organized and effective analysis techniques are essential that can obtain non-trivial, valid, and constructive information. Organizing data into valid groupings is one of the most basic ways of understanding and learning. Cluster analysis is the technique of grouping or clustering objects based on the measured or perceived fundamental features or similarity. The main objective of clustering is to discover structure in data and hence it is exploratory in nature. But the major risk for clustering approaches is to handle the outliers. Outliers occur because of the mechanical faults, any transformation in system behavior, fraudulent behavior, human fault, instrument mistake or any form of natural deviations. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. In this paper, the standard K-Means technique is enhanced using the Greedy algorithm for effective detection and removal of outliers (EKMOD). Experiments on iris dataset revealed that EKMOD automatically detect and remove outliers, and thus help in increasing the clustering accuracy. Moreover, the Means Squared Error and execution time is very less for the proposed EKMOD.