Discovery of Non-Interesting Attribute in Mining Outliers Pattern

Abstract

An outlier in a dataset is a point or a class of points that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of outliers is important for many applications and has always attracted attention among data mining research community. In this paper, we present a new method in detecting outlier by discovering Non-IntAttrb from the information system (IS). Non-IntAttrb is set of attributes from IS that may contain outliers. We discover the computation of Non-IntAttrb by defining indiscemibility matrix modulo (iDMM) and indiscemibility function modulo(iDFM). We define a measurement calledRSetOF(Rough Set Outlier Factor Value) to detect outlier objects. The experimental results show that our approach is a fast outlier detection method.