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Homogenous Granulation and Its Epsilon Variant

Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, 10-710 Olsztyn, Poland
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These authors contributed equally to this work.
Extended version of paper “A Study in Granular Computing: homogenous granulation” presented at the 24rd International Conference on Information and Software Technologies (ICIST 2018), Vilnius, Lithuania, 5–6 October 2018.
Computers 2019, 8(2), 36; https://doi.org/10.3390/computers8020036
Received: 14 February 2019 / Revised: 28 April 2019 / Accepted: 7 May 2019 / Published: 10 May 2019
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Abstract

In the era of Big data, there is still place for techniques which reduce the data size with maintenance of its internal knowledge. This problem is the main subject of research of a family of granulation techniques proposed by Polkowski. In our recent works, we have developed new, really effective and simple techniques for decision approximation, homogenous granulation and epsilon homogenous granulation. The real problem in this family of methods was the choice of an effective parameter of approximation for any datasets. It was resolved by homogenous techniques. There is no need to estimate the optimal parameters of approximation for these methods, because those are set in a dynamic way according to the data internal indiscernibility level. In this work, we have presented an extension of the work presented at ICIST 2018 conference. We present results for homogenous and epsilon homogenous granulation with the comparison of its effectiveness. View Full-Text
Keywords: homogenous granulation; Rough Sets; decision systems; classification homogenous granulation; Rough Sets; decision systems; classification
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Ropiak, K.; Artiemjew, P. Homogenous Granulation and Its Epsilon Variant. Computers 2019, 8, 36.

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