Outlier Detection Algorithms In Data Mining

The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud; Outlier is defined as an observation that deviates too much from other observations. Existing methods that we have seen for finding outliers can only deal efficiently with two dimensions/attributes of a dataset. Many recent algorithms have been proposed for outlier detection that uses several concepts of proximity in order to find the outliers based on their relationship to the other points in the data. However, in high-dimensional space, the data are sparse and concepts using the notion of proximity fail to retain their effectiveness This paper mainly discusses and compares approach of different outlier detection from data mining perspective, which can be categorized into two categories: classic outlier approach and spatial outlier approach