Comparative Analysis of Point Outlier Detection using Cluster Based Techniques

Abstract

Outlier detection is the process of finding peculiar pattern from given set of data. Nowadays,outlier detection is more popular subject in different knowledge domain. Data size is rapidly increases every year there is need to detect outlier in large dataset as early as possible. In this research, comparison of three different cluster based outlier detection algorithm i.e. Kmeans with OD, Partition around medoids (PAM) with OD and density based spatial clustering

algorithm with noise (DBSCAN) is presented. The main aim of this research is to evaluate their

performance

of those three different cluster based outlier algorithm for different dataset with different dimension. The dataset used for this research are chosen such way that they are different in size, mainly in terms of number of instances and attributes. When comparing the performance of all three cluster based outlier detection algorithms, in average PAM with OD is found to bebetter algorithm to detect outlier in most cases with accuracy level 98.30% as well as 92.20% precision, 95.30% recall and 93.43% F-measure value.