INFORMATICA, 2004, Vol. 15, No. 3, 399-410
© Institute of Mathematics and Informatics,
Outlier Detection Based on the Distribution of Distances between Data Points
Institute of Mathematics and Informatics Akademijos 4, 2600 Vilnius, Lithuania E-mail: firstname.lastname@example.org
A novel approach to outlier detection on the ground of the properties of distribution of distances between multidimensional points is presented. The basic idea is to evaluate the outlier factor for each data point. The factor is used to rank the dataset objects regarding their degree of being an outlier. Selecting the points with the minimal factor values can then identify outliers. The main advantages of the approach are: (1) no parameter choice in outlier detection is necessary; (2) detection is not dependent on clustering algorithms.
To demonstrate the quality of the outlier detection, the experiments were performed on widely used datasets. A comparison with some popular detection methods shows the superiority of our approach.
outlier detection, high-dimensional data, distribution of distances
To preview Lithuanian abstract see full article
To preview full
article text in PDF format click here
You could obtain free Acrobat Reader from