Impact of Dimensionality ReductionOn Outlier Detection Techniques

In statistics, an outlier is an observation that is distant from the rest of the data. In other word mining or detecting outliers is referred to sequence of operations that lead to find exceptional objects that deviate from the rest of the data set. In this paper we use a correlation based feature selection algorithm, for dimensionality reduction as a preprocessing phase to outlier detection algorithms, which causes improvement on results of different types of outlier detection techniques.

Keywords:

Outlier Detection , Dimensionality Reduction , Unsupervised Learning