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Data Partitioning—Empirical Approach

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Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
Number of pages24
ISBN (print)9783030023836
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


In this chapter, a new empirical approach, named autonomous data partitioning, is proposed to partition the data autonomously by creating a Voronoi tessellation around the objectively identified prototypes to form data clouds, which transform the large amount of raw data into a much smaller (manageable) number of more representative aggregations with semantic meaning. The proposed empirical algorithm has two forms/types, namely, the offline version and the evolving version. The offline version is based on the ranks of the observations in terms of their multimodal typicality values and local ensemble properties. The evolving version is for streaming data processing and works with the data density. It is able to start “from scratch”, but can create a hybrid with the offline version as well. Moreover, an algorithm is proposed to guarantee the local optimality of the autonomous data partitioning approach allowing the proposed approach to end up with a locally optimal structure of data clouds represented by their focal points/prototypes, which is then ready to be used for analysis, building a multi-model classifier, predictor, controller or for fault isolation. © 2019, Springer Nature Switzerland AG.