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Volumn 7, Issue 6, 2014, Pages 853-857

Comparative study of fuzzy C means and K means algorithm for requirements clustering

Author keywords

Clustering; Data mining; Fuzzy C means; K means; Library; Requirements

Indexed keywords


EID: 84909602414     PISSN: 09746846     EISSN: 09745645     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (25)

References (14)
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    • Lessons learned from open source projects for facilitating online requirements processes
    • Springer Berlin Heidelberg
    • Laurent P, Cleland-Huang J. Lessons learned from open source projects for facilitating online requirements processes. Requirements Engineering: Foundation for Software Quality. Springer Berlin Heidelberg; 2009. p. 240-55.
    • (2009) Requirements Engineering: Foundation for Software Quality , pp. 240-255
    • Laurent, P.1    Cleland-Huang, J.2
  • 4
    • 0031234218 scopus 로고    scopus 로고
    • A cost-value approach for prioritizing requirements
    • Karlsson J, Ryan K. A cost-value approach for prioritizing requirements. Software. 1997; 14(5):67-74.
    • (1997) Software , vol.14 , Issue.5 , pp. 67-74
    • Karlsson, J.1    Ryan, K.2
  • 6
    • 0011403160 scopus 로고    scopus 로고
    • First things first: prioritizing requirements
    • Wiegers K. First things first: prioritizing requirements. Software Development. 1999; 7(9):48-53.
    • (1999) Software Development , vol.7 , Issue.9 , pp. 48-53
    • Wiegers, K.1
  • 7
    • 0033556908 scopus 로고    scopus 로고
    • An on-line agglomerative clustering method for nonstationary data
    • Guedalia ID, London M, Werman M. An on-line agglomerative clustering method for nonstationary data. Neural Computation. 1999; 11(2):521-40.
    • (1999) Neural Computation , vol.11 , Issue.2 , pp. 521-540
    • Guedalia, I.D.1    London, M.2    Werman, M.3
  • 8
    • 0037284901 scopus 로고    scopus 로고
    • Using self-similarity to cluster large data sets
    • Barbará D, Chen P. Using self-similarity to cluster large data sets. Data Mining and Knowledge Discovery. 2003; 7(2):123-52.
    • (2003) Data Mining and Knowledge Discovery , vol.7 , Issue.2 , pp. 123-152
    • Barbará, D.1    Chen, P.2
  • 9
    • 0037401269 scopus 로고    scopus 로고
    • Empirical comparison of fast partitioning-based clustering algorithms for large data sets
    • Wei CP, Lee YH, Hsu CM. Empirical comparison of fast partitioning-based clustering algorithms for large data sets. Expert Systems with applications. 2003; 24(4):351-63.
    • (2003) Expert Systems with applications , vol.24 , Issue.4 , pp. 351-363
    • Wei, C.P.1    Lee, Y.H.2    Hsu, C.M.3
  • 11
    • 77954877262 scopus 로고
    • A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters
    • Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. 1973.
    • (1973)
    • Dunn, J.C.1
  • 13
    • 80053063019 scopus 로고    scopus 로고
    • Comparative Investigations and Performance Analysis of FCM and MFPCM Algorithms on Iris data
    • Rao VS, Vidyavathi DS. Comparative Investigations and Performance Analysis of FCM and MFPCM Algorithms on Iris data. Indian Journal of Computer Science and Engineering. 2010; 1(2):145-51.
    • (2010) Indian Journal of Computer Science and Engineering , vol.1 , Issue.2 , pp. 145-151
    • Rao, V.S.1    Vidyavathi, D.S.2
  • 14
    • 36948999941 scopus 로고    scopus 로고
    • University of California, School of Information and Computer Science, Irvine, CA
    • Asuncion A, Newman DJ. UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA; 2007.
    • (2007) UCI Machine Learning Repository
    • Asuncion, A.1    Newman, D.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.