A New Hybridized K-Means Clustering Based Outlier Detection Technique for Effective Data Mining
Source: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Now-a-days million of databases have been used in business management, Govt., scientific engineering & in many other application & keeps growing rapidly in present day scenario. The explosive growth in data & database has generated an urgent need to develop new technique to remove outliers for effective data mining. In this paper, the authors have suggested a clustering based outlier detection algorithm for effective data mining which uses k-means clustering algorithm to cluster the data sets and Outlier Finding Technique (OFT) to find out outlier on the basis of density based and distance based Outlier Finding Technique.