Mining Open Source Software (OSS) data using association rules network

Sanjay Chawla, Bavani Arunasalam, Joseph Davis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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).

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsKyuseok Shim, Kyu-Young Wang, Jongwoo Jeon, Jaideep Srivastava
PublisherSpringer Verlag
Pages461-466
Number of pages6
ISBN (Electronic)3540047603, 9783540047605
Publication statusPublished - 1 Jan 2003
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: 30 Apr 20032 May 2003

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2637
ISSN (Print)0302-9743

Conference

Conference7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
CountryKorea, Republic of
CitySeoul
Period30/4/032/5/03

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Keywords

  • Association rule
  • Factor analysis
  • Hypergraph clustering
  • Networks
  • Opensource software

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chawla, S., Arunasalam, B., & Davis, J. (2003). Mining Open Source Software (OSS) data using association rules network. In K. Shim, K-Y. Wang, J. Jeon, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 461-466). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637). Springer Verlag.