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Empirical Approach to Machine Learning pp 175–198Cite as

Data Partitioning—Empirical Approach

Part of the Studies in Computational Intelligence book series (SCI,volume 800)

Abstract

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.

Keywords

  • Multimodal Typicality
  • Cloud Data
  • Data Partitioning Approach
  • Voronoi Tessellation
  • Ensemble Properties

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, UK

    Plamen P. Angelov & Xiaowei Gu

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  1. Plamen P. Angelov
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  2. Xiaowei Gu
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Correspondence to Plamen P. Angelov .

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Angelov, P.P., Gu, X. (2019). Data Partitioning—Empirical Approach. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_7

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