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An Efficient Algorithm of Frequent Connected Subgraph Extraction

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Advances in Knowledge Discovery and Data Mining (PAKDD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2637))

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Abstract

Mining frequent patterns from datasets is one of the key success stories of data mining research. Currently, most of the works focus on independent data, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective in this paper. We use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm Topology, which can mine these graphs efficiently, has been proposed. We evaluate the performance of the algorithm by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.

This paper was supported by the Key Program of National Natural Science Foundation of China (No. 69933010) and China National 863 High-Tech Projects (No. 2002AA4Z3430 and 2002AA231041)

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Author information

Authors and Affiliations

  1. Department of Computing and Information Technology Science, Fudan University, Shanghai, 200433, P.R.China

    Mingsheng Hong, Haofeng Zhou, Wei Wang & Baile Shi

Authors
  1. Mingsheng Hong
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  2. Haofeng Zhou
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  3. Wei Wang
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  4. Baile Shi
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Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea

    Kyu-Young Whang

  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea

    Jongwoo Jeon

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA

    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Hong, M., Zhou, H., Wang, W., Shi, B. (2003). An Efficient Algorithm of Frequent Connected Subgraph Extraction. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_5

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  • DOI: https://doi.org/10.1007/3-540-36175-8_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

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