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BigData 2020: Big Data pp 112–122Cite as

Optimal Subspace Analysis Based on Information-Entropy Increment

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1320))

Abstract

The data structure is becoming more and more complex, and the scale of the data set is getting larger and larger. The strong limitations and instability in the high-dimensional data environment is showed in traditional outlier detection method. To solve the problems, an Optimal subspace Analysis based on Information-entropy Increment is proposed. The concepts such as mutual information and dimensional entropy to re-portrait the indicators that measure the pros and cons of subspace clustering, optimize the objective function of the clustering subspace, and obtain the optimal subspace. According to the idea of dividing the information entropy increment by one, the entropy outlier score is proposed as a metric to detect outliers in the optimal subspace. Finally, experiments verify the effectiveness of the algorithm.

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Zhongping Zhang, Iiaojiao Liu, Yuting Zhang & Jiyao Zhang

  2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Zhongping Zhang, Iiaojiao Liu, Yuting Zhang, Jiyao Zhang & Mingru Tian

  3. Hebei Education Examinations Authority, Shijiazhuang, 050000, China

    Mingru Tian

Authors
  1. Zhongping Zhang
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  2. Iiaojiao Liu
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  3. Yuting Zhang
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  4. Jiyao Zhang
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  5. Mingru Tian
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Corresponding author

Correspondence to Zhongping Zhang .

Editor information

Editors and Affiliations

  1. PLA Academy of Military Sciences, Beijing, China

    Hong Mei

  2. Southwest University, Chongqing, China

    Weiguo Zhang

  3. The University of Edinburgh, Edinburgh, UK

    Wenfei Fan

  4. Southwest University, Chongqing, China

    Zili Zhang

  5. Nanjing University, Nanjing, China

    Yihua Huang

  6. Zhejiang University, Hangzhou, China

    Jiajun Bu

  7. Nanjing University, Nanjing, China

    Yang Gao

  8. Taiyuan University of Technology, Taiyuan, China

    Li Wang

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Cite this paper

Zhang, Z., Liu, I., Zhang, Y., Zhang, J., Tian, M. (2021). Optimal Subspace Analysis Based on Information-Entropy Increment. In: Mei, H., et al. Big Data. BigData 2020. Communications in Computer and Information Science, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-16-0705-9_8

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  • DOI: https://doi.org/10.1007/978-981-16-0705-9_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0704-2

  • Online ISBN: 978-981-16-0705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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