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Volumn 41, Issue 3, 2008, Pages 983-994

Comparison between two coevolutionary feature weighting algorithms in clustering

Author keywords

Complex data; Cooperative coevolution; Feature weighting; Modular clustering

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTATIONAL COMPLEXITY; COST FUNCTIONS; EVOLUTIONARY ALGORITHMS; IMAGE CLASSIFICATION; KNOWLEDGE ENGINEERING;

EID: 35448929079     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.07.008     Document Type: Article
Times cited : (16)

References (42)
  • 2
    • 16444383160 scopus 로고    scopus 로고
    • Survey of clustering algorithms
    • Xu R., and Wunsch D. Survey of clustering algorithms. IEEE Trans. Neural Networks 16 3 (2005) 645-678
    • (2005) IEEE Trans. Neural Networks , vol.16 , Issue.3 , pp. 645-678
    • Xu, R.1    Wunsch, D.2
  • 4
    • 21844501258 scopus 로고
    • Weighting and selection of variables for cluster analysis
    • Gnanadesikan R., Kettenring J.R., and Tsao S.L. Weighting and selection of variables for cluster analysis. J. Classification 12 1 (1995) 113-136
    • (1995) J. Classification , vol.12 , Issue.1 , pp. 113-136
    • Gnanadesikan, R.1    Kettenring, J.R.2    Tsao, S.L.3
  • 6
    • 8644255832 scopus 로고    scopus 로고
    • Clustering objects on subsets of attributes
    • Friedman J.H., and Meulman J.J. Clustering objects on subsets of attributes. J. R. Statist. Soc. 66 4 (2004) 815-849
    • (2004) J. R. Statist. Soc. , vol.66 , Issue.4 , pp. 815-849
    • Friedman, J.H.1    Meulman, J.J.2
  • 8
    • 1842762839 scopus 로고    scopus 로고
    • An optimization algorithm for clustering using weighted dissimilarity measures
    • Chan E.Y., Ching W.K., Ng M.K., and Huang J.Z. An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition 37 (2004) 943-952
    • (2004) Pattern Recognition , vol.37 , pp. 943-952
    • Chan, E.Y.1    Ching, W.K.2    Ng, M.K.3    Huang, J.Z.4
  • 9
    • 0346847567 scopus 로고    scopus 로고
    • Unsupervised learning of prototypes and attribute weights
    • Frigui H., and Nasraoui O. Unsupervised learning of prototypes and attribute weights. Pattern Recognition 34 (2004) 567-581
    • (2004) Pattern Recognition , vol.34 , pp. 567-581
    • Frigui, H.1    Nasraoui, O.2
  • 11
  • 14
    • 33646511082 scopus 로고    scopus 로고
    • MACLAW: a modular approach for clustering with local attribute weighting
    • Blansché A., and Gançarski P. MACLAW: a modular approach for clustering with local attribute weighting. Pattern Recognition Lett. 27 11 (2006) 1299-1306
    • (2006) Pattern Recognition Lett. , vol.27 , Issue.11 , pp. 1299-1306
    • Blansché, A.1    Gançarski, P.2
  • 15
    • 34248370390 scopus 로고    scopus 로고
    • Feature subset selection in unsupervised learning via multiobjective optimization
    • Handl J., and Knowles J. Feature subset selection in unsupervised learning via multiobjective optimization. Int. J. Comput. Intell. Res. 2 3 (2006) 217-238
    • (2006) Int. J. Comput. Intell. Res. , vol.2 , Issue.3 , pp. 217-238
    • Handl, J.1    Knowles, J.2
  • 16
    • 0031073477 scopus 로고    scopus 로고
    • A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms
    • Wettschereck D., Aha D.W., and Mohri T. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11 1-5 (1997) 273-314
    • (1997) Artif. Intell. Rev. , vol.11 , Issue.1-5 , pp. 273-314
    • Wettschereck, D.1    Aha, D.W.2    Mohri, T.3
  • 18
    • 0042312608 scopus 로고    scopus 로고
    • Feature weighting in k-means clustering
    • Modha D., and Spangler S. Feature weighting in k-means clustering. Mach. Learn. 52 3 (2003) 217-237
    • (2003) Mach. Learn. , vol.52 , Issue.3 , pp. 217-237
    • Modha, D.1    Spangler, S.2
  • 19
    • 0036538165 scopus 로고    scopus 로고
    • Improving performance of similarity-based clustering by feature weight learning
    • Yeung D.S., and Wang X.Z. Improving performance of similarity-based clustering by feature weight learning. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 556-561
    • (2002) IEEE Trans. Pattern Anal. Mach. Intell. , vol.24 , pp. 556-561
    • Yeung, D.S.1    Wang, X.Z.2
  • 20
    • 84957539111 scopus 로고    scopus 로고
    • N. Howe, C. Cardie, Examining locally varying weights for nearest neighbor algorithms, in: ICCBR, 1997, pp. 455-466.
  • 22
    • 25144493947 scopus 로고    scopus 로고
    • A Lamarckian evolution strategy for genetic algorithms
    • Chambers L.D. (Ed), CRC Press, FL
    • Ross B.J. A Lamarckian evolution strategy for genetic algorithms. In: Chambers L.D. (Ed). Practical Handbook of Genetic Algorithms: Complex Coding Systems vol. 3 (1999), CRC Press, FL 1-16
    • (1999) Practical Handbook of Genetic Algorithms: Complex Coding Systems , vol.3 , pp. 1-16
    • Ross, B.J.1
  • 23
    • 35448969760 scopus 로고    scopus 로고
    • D.E. Goldberg, S. Voessner, Optimizing global-local search hybrids, in: Proceedings of the Genetic and Evolutionary Computation Conference, 1999, pp. 220-228.
  • 25
    • 35448972128 scopus 로고    scopus 로고
    • C.H. Yong, R. Miikkulainen, Cooperative coevolution of multi-agent systems, Technical report, University of Texas at Austin Department of Computer Sciences, 2001.
  • 26
    • 35448941525 scopus 로고    scopus 로고
    • M.A. Potter, K.A. De Jong, Evolving neural networks with collaborative species, in: Proceedings of the 1995 Summer Computer Simulation Conference, The Society of Computer Simulation, 1995, pp. 340-345.
  • 27
    • 0036887498 scopus 로고    scopus 로고
    • Multi-objective cooperative coevolution of artificial neural networks
    • Hervas-Martinez C., Garcia-Pedrajas N., and Munoz-Pèrez J. Multi-objective cooperative coevolution of artificial neural networks. Neural Networks 15 10 (2002) 1259-1278
    • (2002) Neural Networks , vol.15 , Issue.10 , pp. 1259-1278
    • Hervas-Martinez, C.1    Garcia-Pedrajas, N.2    Munoz-Pèrez, J.3
  • 28
    • 33749867996 scopus 로고    scopus 로고
    • A distributed cooperative coevolutionary algorithm for multiobjective optimization
    • Tan K.C., Yang Y.J., and Goh C.K. A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans. Evol. Comput. 10 5 (2006) 527-549
    • (2006) IEEE Trans. Evol. Comput. , vol.10 , Issue.5 , pp. 527-549
    • Tan, K.C.1    Yang, Y.J.2    Goh, C.K.3
  • 29
    • 0346151192 scopus 로고    scopus 로고
    • Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach
    • Delgadoa M.R., Von Zubenb F., and Gomide F. Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach. Fuzzy Sets and Systems 141 (2004) 89-106
    • (2004) Fuzzy Sets and Systems , vol.141 , pp. 89-106
    • Delgadoa, M.R.1    Von Zubenb, F.2    Gomide, F.3
  • 30
    • 31744440533 scopus 로고    scopus 로고
    • An organizational coevolutionary algorithm for classification
    • Jiao L., Liu J., and Zhong W. An organizational coevolutionary algorithm for classification. IEEE Trans. Evol. Comput. 10 1 (2006) 67-80
    • (2006) IEEE Trans. Evol. Comput. , vol.10 , Issue.1 , pp. 67-80
    • Jiao, L.1    Liu, J.2    Zhong, W.3
  • 31
    • 33646768976 scopus 로고    scopus 로고
    • A. Blansché, P. Gançarski, J.J. Korczak, Genetic algorithms for feature weighting: evolution vs. coevolution and darwin vs. lamarck, in: Proceedings of the Fourth Mexican International Conference on AI, Monterrey, Mexique, Lecture Notes in Computer Science, vol. 3789, 2005, pp. 682-691.
  • 34
    • 0343442766 scopus 로고
    • Knowledge acquisition via incremental conceptual clustering
    • Fisher D.H. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2 (1987) 139-172
    • (1987) Mach. Learn. , vol.2 , pp. 139-172
    • Fisher, D.H.1
  • 35
    • 35448956123 scopus 로고    scopus 로고
    • C.L. Blake, C.J. Merz, UCI repository of machine learning databases, 1998. 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
  • 36
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc., Ser. B 39 1 (1977) 1-38
    • (1977) J. R. Statist. Soc., Ser. B , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.1    Laird, N.2    Rubin, D.3
  • 38
  • 39
    • 37649001169 scopus 로고    scopus 로고
    • S. Genaud, P. Gançarski, G. Latu, A. Blansché, C. Rattanapoka, D. Vouriot, Exploitation of a parallel clustering algorithm on commodity hardware with p2p-mpi. J. Supercomputing, 2007, doi: 10.1007/s11227-007-0136-2.
  • 40
    • 0026278621 scopus 로고
    • A review of assessing the accuracy of classifications of remotely sensed data
    • Congalton R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing Environ. 37 (1991) 35-46
    • (1991) Remote Sensing Environ. , vol.37 , pp. 35-46
    • Congalton, R.G.1
  • 41
    • 35448954901 scopus 로고    scopus 로고
    • V.S.N. Prasad, A.G. Faheema, S. Rakshit, Feature selection in example based image retrieval systems, in: ICVGIP 2002, 2002.
  • 42
    • 35448933655 scopus 로고    scopus 로고
    • M. Dash, H. Liu, Feature selection for clustering, in: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2000.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.