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The data clustering on the base of Self-organizing incremental neural networks and Markov clustering algorithm

Keywords:

Yu.S. Fedorenko – Post-Graduate Student, Bauman Moscow State technical University. E-mail: Fedyura1992@yandex.ru Yu.E. Gapanyuk – Ph.D. (Eng.), Associate Professor, Bauman Moscow State technical University. E-mail: gapyu@bmstu.ru


The new clustering technique on the base of Self-organizing incremental neural networks (SOINN) and Markov clustering al-gorithm (MCL) is presented. Existing clustering methods are analyzed and their advantages and disadvantages are identified. The mathematical background of SOINN is considered and possibilities of using this type of neural networks for density esti-mation are discussed. The base architecture of SOINN networks and its algorithm are described. The problems of data clustering with using of SOINN are demonstrated. The expediency of applying normalized mutual information and f-score for clustering quality evaluation is explained. The experimental comparisons of six clustering algorithms, including proposed method, on six test dataset by two clustering measures are conducted. The obtained results are analyzed and the efficiency of proposed clustering technique is proved.
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