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Volumn 6, Issue , 2002, Pages 553-562

An incremental Multi-Centroid, Multi-Run Sampling Scheme for k-medoids-based algorithms

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DATA ACQUISITION; IMAGE PROCESSING; INVARIANCE; PATTERN RECOGNITION;

EID: 2942633848     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (18)
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    • Efficient k-medoids algorithms using multi-centroids with multi-runs sampling scheme
    • Taipei, Taiwan. Springer
    • S-C. Chu, J. F. Roddick, and J. S. Pan. Efficient k-medoids algorithms using multi-centroids with multi-runs sampling scheme. In Workshop on Mining Data for CRM, Taipei, Taiwan, 2002. Springer.
    • (2002) Workshop on Mining Data for CRM
    • Chu, S.-C.1    Roddick, J.F.2    Pan, J.S.3
  • 3
    • 25944458854 scopus 로고    scopus 로고
    • An incremental multi-centroid, multi-run sampling scheme for k-medoids-based algorithms - Extended report
    • KDM Laboratory, Flinders University
    • S-C. Chu, J. F. Roddick, and J. S. Pan. An incremental multi-centroid, multi-run sampling scheme for k-medoids-based algorithms - extended report. Technical Report KDM-02-003, KDM Laboratory, Flinders University, 2002.
    • (2002) Technical Report , vol.KDM-02-003
    • Chu, S.-C.1    Roddick, J.F.2    Pan, J.S.3
  • 4
    • 85170282443 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • E. Simoudis, J. Han, and U. Fayyad, editors, Portland, OR. AAAI Press
    • Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han, and U. Fayyad, editors, Second International Conference on Knowledge Discovery and Data Mining, pages 226-231, Portland, OR, 1996. AAAI Press.
    • (1996) Second International Conference on Knowledge Discovery and Data Mining , pp. 226-231
    • Ester, M.1    Kriegel, H.-P.2    Sander, J.3    Xu, X.4
  • 9
    • 0035392413 scopus 로고    scopus 로고
    • Vector quantization based on genetic simulated annealing
    • H. C. Huang, J. S. Pan, Z. M. Lu, S. H. Sun, and H. M. Hang. Vector quantization based on genetic simulated annealing. Signal Processing, 81(7):1513-1523, 2001.
    • (2001) Signal Processing , vol.81 , Issue.7 , pp. 1513-1523
    • Huang, H.C.1    Pan, J.S.2    Lu, Z.M.3    Sun, S.H.4    Hang, H.M.5
  • 10
    • 0037986928 scopus 로고    scopus 로고
    • Chameleon: A hierarchical clustering algorithm using dynamic modeling
    • G. Karypis, E.-H. Han, and V. Kumar. Chameleon: a hierarchical clustering algorithm using dynamic modeling. Computer, 32:32-68, 1999.
    • (1999) Computer , vol.32 , pp. 32-68
    • Karypis, G.1    Han, E.-H.2    Kumar, V.3
  • 12
    • 26444479778 scopus 로고
    • Optimization by simulated annealing
    • S. Kirkpatrick, C.D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing. Science, 220 (4598):671-680, 1983.
    • (1983) Science , vol.220 , Issue.4598 , pp. 671-680
    • Kirkpatrick, S.1    Gelatt Jr., C.D.2    Vecchi, M.P.3
  • 13
    • 0033281354 scopus 로고    scopus 로고
    • A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering
    • Seoul, Korea
    • R. Krishnapuram, A. Joshi, and L. Yi. A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering. In IEEE International Fuzzy Systems Conference, pages 1281-1286, Seoul, Korea, 1999.
    • (1999) IEEE International Fuzzy Systems Conference , pp. 1281-1286
    • Krishnapuram, R.1    Joshi, A.2    Yi, L.3
  • 15
    • 0027427675 scopus 로고
    • On k-medoid clustering of large data sets with the aid of a genetic algorithm: Background, feasibility and comparison
    • C. B. Lucasius, A. D. Dane, and G. Kateman. On k-medoid clustering of large data sets with the aid of a genetic algorithm: background, feasibility and comparison. Analytica Chimica Acta, pages 647-669, 1993.
    • (1993) Analytica Chimica Acta , pp. 647-669
    • Lucasius, C.B.1    Dane, A.D.2    Kateman, G.3
  • 17
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    • Efficient and effective clustering methods for spatial data mining
    • J.B. Bocca, M. Jarke, and C. Zaniolo, editors, Santiago, Chile. Morgan Kaufmann
    • R.T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In J.B. Bocca, M. Jarke, and C. Zaniolo, editors, Twentieth International Conference on Very Large Data Bases, pages 144-155, Santiago, Chile, 1994. Morgan Kaufmann.
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    • Ng, R.T.1    Han, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.