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Rules Extraction Based on Data Summarisation Approach Using DARA

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Advanced Data Mining and Applications (ADMA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5139))

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Abstract

This paper helps the understanding and development of a data summarisation approach that summarises structured data stored in a non-target table that has many-to-one relations with the target table. In this paper, the feasibility of data summarisation techniques, borrowed from the Information Retrieval Theory, to summarise patterns obtained from data stored across multiple tables with one-to-many relations is demonstrated. The paper describes the Dynamic Aggregation of Relational Attributes (DARA) framework, which summarises data stored in non-target tables in order to facilitate data modelling efforts in a multi-relational setting. The application of the DARA algorithm involving structured data is presented in order to show the adaptability of this algorithm to real world problems.

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References

  1. Alfred, R., Kazakov, D.: Data Summarisation Approach to Relational Domain Learning Based on Frequent Pattern to Support the Development of Decision Making. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 889–898. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  2. Kuentzer, J., Backes, C., Blum, T., Gerasch, A., Kaufmann, M., Kohlbacher, O., Lenhof, H.P.: BNDB - The Biochemical Network Database. BMC Bioinformatics 8(1) (2007)

    Google Scholar 

  3. Soon, M.C., Pyeong, S.M., Junguk, L.K.: Integration of a Relational Database with Multimedia Data. Compsac., vol. 00. IEEE Computer Society, Los Alamitos (1996)

    Google Scholar 

  4. Claudia, P., Foster, P.: Distribution-based Aggregation for Relational Learning with Identifier Attributes. Machine Learning 62(1-2), 65–105 (2006)

    Article  Google Scholar 

  5. Claudia, P., Foster, P.: Aggregation-Based Feature Invention and Relational Concept Classes. In: KDD, pp. 167–176 (2003)

    Google Scholar 

  6. Knobbe, A.J., de Haas, M., Siebes, A.: Propositionalisation and Aggregates. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 277–288. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  7. Tremblay, M.C., Fuller, R., Berndt, D., Studnicki, J.: Doing More with More Information: Changing Healthcare Planning with OLAP tools. Decision Support System 43(4), 1305–1320 (2007)

    Article  Google Scholar 

  8. Couturier, O., Delalin, H., Fu, H., Edouard, G.: A Three Step Approach for STULONG Database Analysis: Characterisation of Patients’s Groups. In: Proceeding of the ECML/PKDD 2004 Challenge (2004)

    Google Scholar 

  9. Correa, E., Plastino, A.: Mining Strong Associations and Exceptions in the STULONG Data Set. In: Proceeding of the ECML/PKDD 2004 Challenge (2004)

    Google Scholar 

  10. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  11. Blatak, J.: Mining First-Order Frequent Patterns in the STULONG Database. In: Proceeding of the ECML/PKDD 2004 Challenge (2004)

    Google Scholar 

  12. Van Assche, A., Verbaeten, S., Krzywania, D., Struyf, J., Blockeel, H.: Attribute-Value and First Order Data Mining within the STULONG Project. In: Proceedings of the ECML/PKDD 2003 Workshop on Discovery Challenge, pp. 108–119 (2003)

    Google Scholar 

  13. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  14. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  15. Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company, New York (1984)

    Google Scholar 

  16. Alfred, R., Kazakov, D.: Discretisation Numbers for Multiple-Instances Problem in Relational Database. In: Eleventh East-European Conference on Advances in Databases and Information Systems, pp. 55–65 (2007)

    Google Scholar 

  17. Alfred, R., Kazakov, D.: Clustering Approach to Generalised Pattern Identification Based on Multi-Instanced Objects with DARA. In: Eleventh East-European Conference on Advances in Databases and Information Systems (2007)

    Google Scholar 

  18. Alfred, R.: DARA: Data Summarisation with Feature Construction. In: Second Asia International Conference on Modelling and Simulation AMS 2008, Kuala Lumpur, Malaysia (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, Universiti Malaysia Sabah, Locked Bag 2073, 88999, Kota Kinabalu, Sabah, Malaysia

    Rayner Alfred

Authors
  1. Rayner Alfred
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Editor information

Editors and Affiliations

  1. School of Computer Science, Sichuan University, 610065, Chengdu, China

    Changjie Tang

  2. Department of Computer Science, The University of Western Ontario, Canada

    Charles X. Ling

  3. School of ITEE, The University of Queensland, Australia

    Xiaofang Zhou

  4. Faculty of Science & Engineering, York University, 355 Lumbers Building, M3J 1P3, Toronto, Ontario, Canada

    Nick J. Cercone

  5. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, 4072, Queensland, Australia

    Xue Li

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© 2008 Springer-Verlag Berlin Heidelberg

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Alfred, R. (2008). Rules Extraction Based on Data Summarisation Approach Using DARA. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_54

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  • DOI: https://doi.org/10.1007/978-3-540-88192-6_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88191-9

  • Online ISBN: 978-3-540-88192-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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