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Volumn 49, Issue 3, 2007, Pages 244-254

Clustering large software systems at multiple layers

Author keywords

Categorical; Graph; MULIC; Multiple layer; Software clustering

Indexed keywords

ALGORITHMS; COMPUTER SOFTWARE; GRAPH THEORY; INFORMATION RETRIEVAL;

EID: 33846415512     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2006.10.010     Document Type: Article
Times cited : (28)

References (20)
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  • 5
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  • 7
    • 27144536001 scopus 로고    scopus 로고
    • Extensions to the k-Means algorithm for clustering large data sets with categorical values
    • Huang Z. Extensions to the k-Means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2 3 (1998) 283-304
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.3 , pp. 283-304
    • Huang, Z.1
  • 9
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    • R. Koschke, T. Eisenbarth, A framework for experimental evaluation of clustering techniques, in: Proceedings of the Eighth International Workshop on Program Comprehension, June 2000, pp. 201-210.
  • 10
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  • 11
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    • S. Mancoridis, B.S. Mitchell, Y. Chen, E.R. Gansner, Bunch: a clustering tool for the recovery and maintenance of software system structures, in: Proceedings of the International Conference on Software Engineering (ICSM'99), 1999.
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    • B.S. Mitchell, S. Mancoridis, Comparing the decompositions produced by software clustering algorithms using similarity measurements, in: Proceedings of the International Conference on Software Maintenance (ICSM'01), 2001, pp. 744-753.
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    • R.W. Schwanke, An intelligent tool for re-engineering software modularity, in Proceedings of the 13th International Conference on Software Engineering, May 1991, pp. 83-92.
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    • http://www.swat.uwaterloo.ca/~swagkit.
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    • V. Tzerpos, R.C. Holt, ACDC: an algorithm for comprehension-driven clustering, in: Proceedings of the Seventh Working Conference on Reverse Engineering (WCRE'00), 2000, pp. 258-267.
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  • 19
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  • 20
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.