Separation in Data Mining Based on Fractal Nature of Data

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Provided by: Czech Technical University
Topic: Data Management
Format: PDF
The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. The authors use a singularity exponent in classifiers that are based on distances of patterns to a given (classified) pattern. The approximation of so called probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in the form of a scaling exponent power of a distance is presented together with a way how to state it. Considering data as points in a metric space, three methods are based on transformed distances of neighbors of a given point in a multidimensional space via functions that use different estimates of scaling exponent.
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