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International Conference on Advanced Data Mining and Applications

ADMA 2013: Advanced Data Mining and Applications pp 71–82Cite as

eDARA: Ensembles DARA

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8347))

Abstract

The ever-growing amount of digital data stored in relational databases resulted in the need for new approaches to extract useful information from these databases. One of those approaches, the DARA algorithm, is designed to transform data stored in relational databases into a vector space representation utilising information retrieval theory. The DARA algorithm has shown to produce improvements over other state-of-the-art approaches. However, the DARA suffers a major drawback when the cardinality of attributes in relations are very high. This is because the size of the vector space representation depends on the number of unique values of all attributes in the dataset. This issue can be solved by reducing the number of features generated from the DARA transformation process by selecting only part of the relevant features to be processed. Since relational data is transformed into a vector space representation (in the form of TF-IDF), only numerical values will be used to represent each record. As a result, discretizing these numerical attributes may also reduce the dimensionality of the transformed dataset. When clustering is applied to these datasets, clustering results of various dimensions may be produced as the number of bins used to discretize these numerical attributes is varied. From these clustering results, a final consensus clustering can be applied to produce a single clustering result which is a better fit, in some sense, than the existing clusterings. In this study, an ensemble DARA clustering approach that provides a mechanism to represent the consensus across multiple runs of a clustering algorithm on the relational datasets is proposed.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia

    Chung Seng Kheau, Rayner Alfred & HuiKeng Lau

Authors
  1. Chung Seng Kheau
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  2. Rayner Alfred
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  3. HuiKeng Lau
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Editor information

Editors and Affiliations

  1. US Air Force Office of Scientific Research, 106-0032, Tokyo, Japan

    Hiroshi Motoda

  2. School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China

    Zhaohui Wu

  3. Faculty of Engineering and Information Technology, University of Technology, Chippendale, 2008, Sydney, NSW, Australia

    Longbing Cao

  4. Department of Computing Science, Edmonton, University of Alberta, T6G 2E8, Canada

    Osmar Zaiane

  5. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Min Yao

  6. School of Computer Science, Fudan University, 200433, Shanghai, China

    Wei Wang

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© 2013 Springer-Verlag Berlin Heidelberg

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Kheau, C.S., Alfred, R., Lau, H. (2013). eDARA: Ensembles DARA. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-53917-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53916-9

  • Online ISBN: 978-3-642-53917-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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