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Data Partitioning—Empirical Approach

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

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Data Partitioning—Empirical Approach. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 175-198 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

Harvard

Angelov, PP & Gu, X 2019, Data Partitioning—Empirical Approach. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 175-198. https://doi.org/10.1007/978-3-030-02384-3_7

APA

Angelov, P. P., & Gu, X. (2019). Data Partitioning—Empirical Approach. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 175-198). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_7

Vancouver

Angelov PP, Gu X. Data Partitioning—Empirical Approach. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 175-198. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_7

Author

Angelov, P.P. ; Gu, X. / Data Partitioning—Empirical Approach. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 175-198 (Studies in Computational Intelligence).

Bibtex

@inbook{d53ad423e91f43a38c8aa609728be543,
title = "Data Partitioning—Empirical Approach",
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. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_7",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "175--198",
editor = "Plamen Angelov and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Data Partitioning—Empirical Approach

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-02384-3_7

DO - 10.1007/978-3-030-02384-3_7

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 175

EP - 198

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -