Filtered Clustering Based on Local Outlier Factor in Data Mining

Abstract

In this paper, the impact of -means and local outliner factor on data set is studied. Outlier is the observation which is different from or inconsistent with the rest of the data. However, the main challenges of outlier detection are increasing complexity due to variety of datasets and size of dataset. To evaluate the outlierness and catch similar outliers as a group are also issues of this technique. The concept of (Local Outlier Factor) is presented in this work. The paper describes comparative study of five different methodologies using -means as the base algorithm along with the various distances method used in finding the dissimilarities between the objects hence to analyze the effects of the outliers on the cluster analysis of dataset in data mining.