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Intelligent Data Processing Based on Multi-Dimensional Numbered Memory Structures
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Author(s): Krassimir Markov (Institute of Mathematics and Informatics at BAS, Bulgaria), Koen Vanhoof (Hasselt University, Belgium), Iliya Mitov (Institute of Information Theories and Applications, Bulgaria), Benoit Depaire (Hasselt University, Belgium), Krassimira Ivanova (University for National and World Economy, Bulgaria), Vitalii Velychko (V.M.Glushkov Institute of Cybernetics, Ukraine)and Victor Gladun (V.M.Glushkov Institute of Cybernetics, Ukraine)
Copyright: 2013
Pages: 29
Source title:
Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch022
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Abstract
The Multi-layer Pyramidal Growing Networks (MPGN) are memory structures based on multidimensional numbered information spaces (Markov, 2004), which permit us to create association links (bonds), hierarchically systematizing, and classification the information simultaneously with the input of it into memory. This approach is a successor of the main ideas of Growing Pyramidal Networks (Gladun, 2003), such as hierarchical structuring of memory that allows reflecting the structure of composing instances and gender-species bonds naturally, convenient for performing different operations of associative search. The recognition is based on reduced search in the multi-dimensional information space hierarchies. In this chapter, the authors show the advantages of using the growing numbered memory structuring via MPGN in the field of class association rule mining. The proposed approach was implemented in realization of association rules classifiers and has shown reliable results.
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