Author : Amandeep Kaur and Kamaljit Kaur

Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. Outlier detection has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background, compared with outlier detection approach is still rare. Most sophisticated methods in data mining address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. The identification of outliers can lead to the discovery of unexpected knowledge in areas such as credit card fraud detection, calling card fraud detection, discovering criminal behaviors, discovering computer intrusion, etc. The greedy approach to develop two efficient algorithms , ITB-SS,ITB-SP that provide practical solutions to the optimization problem for outlier detection. For more optimized data in this paper a new work, which is used both algorithms with genetic algorithm which provide more accurate results as compare to previous results.

Keywords: Information theoretic algorithms, Genetic algorithm, Ant colony Optimization, Particle Swarm Optimization

Article published in International Journal of Current Engineering and Technology, Vol.5, No.1 (Feb-2015)