Finding Frequent Subgraphs from Graph Structured Data with Geometric Information and Its Application to Lossless Compression (original) (raw)

Abstract

In this paper, we present an effective algorithm for extracting characteristic substructures from graph structured data with geometric information, such as CAD, map data and drawing data. Moreover, as an application of our algorithm, we give a method of lossless compression for such data. First, in order to deal with graph structured data with geometric information, we give a layout graph which has the total order on all vertices. As a knowledge representation, we define a layout term graph with structured variables. Secondly, we present an algorithm for finding frequent connected subgraphs in given data. This algorithm is based on levelwise strategies like Apriori algorithm by focusing on the total order on vertices. Next, we design a method of lossless compression of graph structured data with geometric information by introducing the notion of a substitution in logic programming. In general, analyzing large graph structured data is a time consuming process. If we can reduce the number of vertices without loss of information, we can speed up such a heavy process. Finally, in order to show an effectiveness of our method, we report several experimental results.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Proc. of the 20th VLDB Conference, pages 487–499, 1994.
    Google Scholar
  2. D. J. Cook and L. B. Holder. Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research, 1:231–255, 1994.
    Google Scholar
  3. D. J. Cook and L. B. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2):32–41, 2000.
    Article Google Scholar
  4. A. Inokuchi, T. Washio, and H. Motoda. An Apriori-based algorithm for mining frequent substructures from graph data. Proc. PAKDD-2000, Springer-Verlag, LNAI 1805, pages 13–23, 2000.
    Google Scholar
  5. T. Matsuda, T. Horiuchi, H. Motoda, and T. Washio. Extension of graph-based induction for general graph structured data. Proc. PAKDD-2000, Springer-Verlag, LNAI 1805, pages 420–431, 2000.
    Google Scholar
  6. T. Miyahara, T. Uchida, T. Shoudai, T. Kuboyama, K. Takahashi, and H. Ueda. Discovering knowledge from graph structured data by using refutably inductive inference of formal graph systems. IEICE Trans. Inf. Syst., E84-D(1):48–56, 2001.
    Google Scholar
  7. T. Uchida, Y. Itokawa, T. Shoudai, T. Miyahara, and Y. Nakamura. A new framework for discovering knowledge from two-dimensional structured data using layout formal graph system. Proc. ALT-00, Springer-Verlag, LNAI 1968, pages 141–155, 2000.
    Google Scholar
  8. T. Uchida, T. Shoudai, and S. Miyano. Parallel algorithm for refutation tree problem on formal graph systems. IEICE Trans. Inf. Syst., E78-D(2):99–112, 1995.
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. Faculty of Human and Social Environment, Hiroshima International University, Kurose, 724-0695, Japan
    Yuko Itokawa
  2. Faculty of Information Sciences, Hiroshima City University, Hiroshima, 731-3194, Japan
    Tomoyuki Uchida, Tetsuhiro Miyahara & Yasuaki Nakamura
  3. Department of Informatics, Kyushu University 39, Kasuga, 816-8580, Japan
    Takayoshi Shoudai

Authors

  1. Yuko Itokawa
  2. Tomoyuki Uchida
  3. Takayoshi Shoudai
  4. Tetsuhiro Miyahara
  5. Yasuaki Nakamura

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

Rights and permissions

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Itokawa, Y., Uchida, T., Shoudai, T., Miyahara, T., Nakamura, Y. (2003). Finding Frequent Subgraphs from Graph Structured Data with Geometric Information and Its Application to Lossless Compression. 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\_58

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us