Title: FRACTAL BASED DATA SEPARATION IN DATA MINING

Year of Publication: 2013
Page Numbers: 287-295
Authors: Marcel Jirina, Marcel Jirina Jr.
Conference Name: The Third International Conference on Digital Information Processing and Communications (ICDIPC2013)
- United Arab Emirates

Abstract:


The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. Considering data as points in a metric space, the methods are based on transformed distances of neighbors of a given point in a multidimensional space via a function that uses an estimate of scaling exponent. We shortly describe them and show that transformation function has form of the distance to the scaling exponent power. We also show the efficiency of methods presented on artificial as well as on real-life tasks and compare them with other standard as well as advanced approaches.