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A new cognitive filtering approach based on Freeman K3 Neural Networks

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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.

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Notes

  1. Java K-sets v1.1.0. doi:10.5281/zenodo.13680 [59]

  2. A trapezoidal numerical integration was performed through MATLAB function trapz(Y).

  3. Em relation to f b 1 6 8 4 dataset the improvement is much better: 27 %.

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Acknowledgments

We thank Dr. R. Kozma for his participation in previous versions of this paper and contributions to K-models. Also, discussions with Dr. D. Dasgupta and Dr. D. Ferebee on clustering in social networks are greatly appreciated. João Luís G. Rosa is grateful to the Brazilian Agency FAPESP (process 2012/09268-3) for the financial support. Denis R. M. Piazentin is grateful to Brazilian Agencies CAPES (process DS-7902068/M) and FAPESP (process 2012/15178-7). In addition, we would like to thank the anonymous reviewers for their valuable suggestions and comments.

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Garcia Rosa, J.L., M. Piazentin, D.R. A new cognitive filtering approach based on Freeman K3 Neural Networks. Appl Intell 45, 363–382 (2016). https://doi.org/10.1007/s10489-016-0772-4

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