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Identifying anomalous values in the realworld database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outliers and influential points is an important step of the regression diagnostics. In this paper, our aimdoi:10.7815/ijorcs.22.2012.018 fatcat:yf3ypp5hcrhl3l7xghvzdro5ly