Homogenous Granulation and Its Epsilon Variant

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 an...

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Bibliographic Details
Main Authors: Krzysztof Ropiak, Piotr Artiemjew
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Computers
Subjects:
Online Access:https://www.mdpi.com/2073-431X/8/2/36
Description
Summary: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.
ISSN:2073-431X