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Time-Series Prediction with Cloud Models in DMKD

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

A growing attention has been paid to mining time-series knowledge, while time-series prediction becomes one of the important aspects of data mining and knowledge discovery (DMKD). This paper presents a new mechanism of time-series prediction with cloud models. This mechanism not only synthesizes different predictive knowledge with different granularities, but also combines two kinds of predictive strategy: local prediction and overall prediction. We focus this paper on the application of cloud models to transform between quantitative and qualitative knowledge, synthesize different kinds of knowledge and realize the soft inference.

Research was supported by the National Advanced Technology Development Projects (No.863-306-ZT06-07-2).

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Author information

Authors and Affiliations

  1. Institute of Communication Engineering, No.2 Biaoyin, Nanjing, China, 210016

    Rong Jiang & Hui Chen

  2. Institute of Electronic System Engineering, No.6, Wanshou Road, Beijing, China, 100036

    Deyi Li

Authors
  1. Rong Jiang
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  2. Deyi Li
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  3. Hui Chen
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Jiang, R., Li, D., Chen, H. (1999). Time-Series Prediction with Cloud Models in DMKD. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_72

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  • DOI: https://doi.org/10.1007/3-540-48912-6_72

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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