Cluster Based Outlier Detection

Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data mining. The proposed approach to detect outlier includes three methods which are clustering, pruning and computing outlier score. For clustering k-means algorithm is used which partition the dataset into given number of clusters. In pruning, based on some distance measure, points which are closed to centroid of each cluster are pruned. For the unpruned points, Local Distance based Outlier Factor (LDOF) measure is calculated. A measure called LDOF, tells how much a point is deviating from its neighbors. The high LDOF value of a point indicates that the point is deviating more from its neighbors and probably it may be an outlier.

Provided by: International Journal of Computer Applications Topic: Big Data Date Added: Nov 2012 Format: PDF

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