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Outlier Detection in Spatial Databases Using Clustering Data Mining

Data mining refers to extracting or “mining” knowledge from large amounts of data. Thus, it plays an important role in extracting spatial patterns and features. It is an essential process where intelligent methods are applied in order to extract data patterns. In this paper, we have proposed a technique with which it is possible to detect whether a given data set is erroneous. Furthermore, our technique locates the possible errors and comprehends the pattern of errors to minimize outliers. Finally, it ensures the integrity and correctness of large databases. We have made use of some of the existing clustering algorithms (like PAM, CLARA, CLARANS) to formulate our proposed technique. The proposed outlier detection and minimization method is simpler to implement, efficient comparing with respect to both time and memory complexity than other existing methods.