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Application of Data Mining for Behavior Pattern Recognition in Telecommunication

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Book cover Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

In telecom industry, mobile subscribers produce data traffic while online every day. These data traffic suggests that certain characteristics of the behavior. The application of data mining helped to analyze and identify the features from the data traffic. In this paper, we use exponential binning of data preprocessing technology to smooth the data sets and keep reduce the noise. By using K-means algorithm to cluster the data traffic stream, we aim to mining subscribers’ behavior characteristics from clusters, provide support for churn prediction, target marketing, and fraud detection.

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Author information

Authors and Affiliations

  1. China Mobile Group Jiangsu Co., Ltd, Nanjing, China

    Xingshen Wu, Yu Zhao & Qiang Gu

  2. China Mobile Research Institute, Beijing, China

    Li Gao

Authors
  1. Xingshen Wu
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  2. Yu Zhao
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  3. Qiang Gu
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  4. Li Gao
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Corresponding author

Correspondence to Xingshen Wu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Wu, X., Zhao, Y., Gu, Q., Gao, L. (2018). Application of Data Mining for Behavior Pattern Recognition in Telecommunication. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_40

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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