Outlier detection based on transitive closure

Outlier detection is an important task in data mining because outliers may bring either new knowledge or potential threats. Much of recent research has focused on measuring the local difference between an outlier and its nearest neighbors, some of which may be unsuitable reference objects. Thus, local difference cannot represent true outlying-ness. On the basis of this conclusion, we propose a new outlying-ness measure that reflects the connectivity of any object to the main body of a data set. For any object p, the outlying-ness is denoted by the connectivity from the k-th most similar neighbor to p. The proposed measure is applicable to arbitrary-density and arbitrarily-shaped data. It is uninfluenced by unsuitable reference objects and effectively identifies outlying clusters without the need for clustering algorithms and additional parameters.