Outlying data points are generally found through two different approaches either being a distance (Knorr & Ng, 1998; Knorr & Ng, 1999; Ramaswamy et al, 2000) or density method (Jin et al, 2001; Papadimitriou et al, 2003). These techniques find outliers based on the entire space of the data set. But in the previous few years the stage has shifted.
We are not concerned with the data points that deviate in the entire space. But instead we are focusing on how a data point becomes an outlier or special when looking at it through different dimensions . A great deal of effort is being invested into the research of outlying subspaces due to the fact that, as a data set increases with a high set of dimensions. It becomes computationally expensive and time consuming to