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Volumn 3488 LNAI, Issue , 2005, Pages 228-236

On Autonomous κ-means clustering

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; PROBLEM SOLVING;

EID: 26944500189     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11425274_24     Document Type: Conference Paper
Times cited : (10)

References (20)
  • 1
    • 0343442766 scopus 로고
    • Knowledge acquisition via incremental conceptual clustering
    • Fisher, D.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2 (1987) 139-172
    • (1987) Machine Learning , vol.2 , pp. 139-172
    • Fisher, D.1
  • 3
    • 0001654728 scopus 로고
    • On convergence of k-means and partitions with minimum average variance (abstract)
    • MacQueen, J.B.: On convergence of k-means and partitions with minimum average variance (abstract). Annals of Mathematical Statistics 36 (1965) 1084
    • (1965) Annals of Mathematical Statistics , vol.36 , pp. 1084
    • MacQueen, J.B.1
  • 4
    • 0000014486 scopus 로고
    • Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications
    • Forgy, E.: Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics 21 (1965) 768
    • (1965) Biometrics , vol.21 , pp. 768
    • Forgy, E.1
  • 8
    • 0001820920 scopus 로고    scopus 로고
    • X-means: Extending k-means with efficient estimation of the number of clusters
    • Morgan Kaufmann
    • Pelleg, D., Moore, A.: X-means: Extending k-means with efficient estimation of the number of clusters. In: Proc. 17th Intl. Conf. on Machine Learning, Morgan Kaufmann (2000) 727-734
    • (2000) Proc. 17th Intl. Conf. on Machine Learning , pp. 727-734
    • Pelleg, D.1    Moore, A.2
  • 10
    • 0003136237 scopus 로고
    • Efficient and effective clustering methods for spatial data mining
    • Morgan Kaufmann
    • Ng, R.T., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proc. 20th Intl. Conf. on Very Large Data Bases, Morgan Kaufmann (1994) 144-155
    • (1994) Proc. 20th Intl. Conf. on Very Large Data Bases , pp. 144-155
    • Ng, R.T.1    Han, J.2
  • 13
    • 1942419246 scopus 로고    scopus 로고
    • The anchors hierarchy: Using the triangle inequality to survive high dimensional data
    • Morgan Kaufmann
    • Moore, A.W.: The anchors hierarchy: Using the triangle inequality to survive high dimensional data. In: Proc. 16th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann (2000) 397-405
    • (2000) Proc. 16th Conf. on Uncertainty in Artificial Intelligence , pp. 397-405
    • Moore, A.W.1
  • 14
    • 1942485278 scopus 로고    scopus 로고
    • Using the triangle inequality to accelerate k-means
    • AAAI Press
    • Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proc. 20th Intl. Conf. on Machine Learning, AAAI Press (2003) 147-153
    • (2003) Proc. 20th Intl. Conf. on Machine Learning , pp. 147-153
    • Elkan, C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.