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Mining Open Source Software (OSS) Data Using Association Rules Network

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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

The Open Source Software(OSS) movement has attracted considerable attention in the last few years. In this paper we report our results of mining data acquired from SourceForge.net, the largest open source software hosting website. In the process we introduce Association Rules Network(ARN), a (hyper)graphical model to represent a special class of association rules. Using ARNs we discover important relationships between the attributes of successful OSS projects. We verify and validate these relationships using Factor Analysis, a classical statistical technique related to Singular Value Decomposition(SVD).

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Author information

Authors and Affiliations

  1. Knowledge Management Research Group, School of Information Technologies, University of Sydney, NSW, Australia

    Sanjay Chawla, Bavani Arunasalam & Joseph Davis

Authors
  1. Sanjay Chawla
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  2. Bavani Arunasalam
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  3. Joseph Davis
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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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Chawla, S., Arunasalam, B., Davis, J. (2003). Mining Open Source Software (OSS) Data Using Association Rules Network. 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_46

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  • DOI: https://doi.org/10.1007/3-540-36175-8_46

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

  • eBook Packages: Springer Book Archive

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