A Framework for Outlier Detection in Geographic Spatial Data

Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geographic data. In this paper an efficient clustering and density based outlier detection framework has been proposed. The process of outlier detection has been categorized into two steps in the first step data has been clustered together based on any density based DBSCAN algorithm and in the second stage outlier detection is performed using LOF. The purpose is to perform clustering and outlier mining simultaneously to improve feasibility of framework. To verify the efficiency and robustness of proposed method, comparative study of proposed approach and several existing approaches are presented in detail, various simulation results demonstrate the effectiveness of the proposed approach.