A New Hybridized K-Means Clustering Based Outlier Detection Technique for Effective Data Mining

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.

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Topic: Big Data Date Added: Apr 2012 Format: PDF

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