A new cognitive filtering approach based on Freeman K3 Neural Networks

Author(s)

Garcia Rosa, Joao Luis ; Piazentin, Denis R. M.

• Document type: Journal article
• Source: APPLIED INTELLIGENCE; v. 45, n. 2, p. 363-382, SEP 2016.
• Web of Science Citations: 3

Abstract

Huge volume of data over several domains demands the development of new more efficient tools for search, analysis, and interpretation. Clustering approaches represent an important step in exploring the internal structure and relationships in datasets. In this study, the cognitively motivated neural network Freeman K (3)-set was applied as a filter to preprocess the data, achieving a better clustering performance. We combine K (3) with a variety of clustering algorithms commonly used, and tested its performance using standard UCI datasets and also datasets from social networks. A comprehensive evaluation using a number of cluster validation measures shows significant improvement in the overall performance of the K (3)-based clustering method for social data sets, for two types of clustering validation measures. Additionally, K (3) filtering results in transparent representation of data, which leads to improved efficiency of data processing algorithms used. (AU)

• FAPESP's process: 12/15178-7 - Continuous electroencephalogram classification using a Connectionist Model based on Populations of Neurons
• Grantee: Denis Renato de Moraes Piazentin
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 12/09268-3 - Brain computational models based on neurodynamical populations at mesoscopic level
• Grantee: João Luís Garcia Rosa
• Support Opportunities: Scholarships abroad - Research