Measure of Similarity and Compactness in Competitive Space

Authors:
Nikolay Zagoruiko
Affiliations:
Institute of Mathematics of the Siberian Devision, of the Russian Academy of Sciences, Novosibirsk, Russia 630090
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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Abstract

The given work is devoted to measures of similarity which are used at discovering of empirical regularities (knowledge). The function of competitive (rival) similarity (FRiS) is proposed as a similarity measure for classification and pattern recognition applications. This function allows one to design effective algorithms for solving all basic data mining tasks, obtain quantitative estimates of the compactness of patterns and the informativeness of feature spaces, and construct easily interpretable decision rules. The method is suitable for any number of patterns regardless of the nature of their distributions and conditionality of training samples (the ratio of the numbers of objects and features). The usefulness of the FRiS is shown by solving a problems of molecular biology.