Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16202
Title: Intelligent Data Processing Based on Multi-Dimensional Numbered Memory Structures
Authors: MARKOV, Krassimir 
VANHOOF, Koen 
MITOV, Iliya 
DEPAIRE, Benoit 
IVANOVA, Krassimira 
Velychko, Vitalii
Gladun, Victor
Issue Date: 2013
Publisher: IGI Global
Source: Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems, p. 156-184
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.
Document URI: http://hdl.handle.net/1942/16202
ISBN: 978 1-4666-1900-5
DOI: 10.4018/978-1-4666-1900-5.ch007
Category: B2
Type: Book Section
Validations: vabb 2017
Appears in Collections:Research publications

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