Inproceedings (1283494)
Arthur, D. & Vassilvitskii, S.
k-means++: the advantages of careful seeding
Society for Industrial and Applied Mathematics, 2007, 1027-1035

Inproceedings (Bickel&Scheffer04)
Bickel, S. & Scheffer, T.
Multi--View Clustering
2004

Article (journals/ida/SavaresiB04)
Savaresi, S. M. & Boley, D.
A comparative analysis on the bisecting K-means and the PDDP clustering algorithms.
Intell. Data Anal., 2004, 8, 345-362

Inproceedings (hotho03wordnet)
Hotho, A.; Staab, S. & Stumme, G.
Wordnet improves text document clustering
2003

Inproceedings (584877)
Zhao, Y. & Karypis, G.
Evaluation of hierarchical clustering algorithms for document datasets
ACM Press, 2002, 515-524

Inproceedings (658040)
Hotho, A.; Maedche, A. & Staab, S.
Text Clustering Based on Good Aggregations
IEEE Computer Society, 2001, 607-608

Inproceedings (conf/icml/WagstaffCRS01)
Wagstaff, K.; Cardie, C.; Rogers, S. & Schrödl, S.
Constrained K-means Clustering with Background Knowledge.
2001, 577-584

Inproceedings (kirsten2000relkmeans)
Kirsten, M. & Wrobel, S.
Cussens, J. & Frisch, A. M. (ed.)
Extending K-Means Clustering to First-Order Representations
Springer, 2000, 1866, 112-129

Inproceedings (pelleg00xmeans)
Pelleg, D. & Moore, A.
$X$-means: Extending $K$-means with Efficient Estimation of the Number of Clusters
Morgan Kaufmann, San Francisco, CA, 2000, 727-734

Inproceedings (steinbach00comparison)
Steinbach, M.; Karypis, G. & Kumar, V.
A comparison of document clustering techniques
2000

Inproceedings (steinbach00)
Steinbach, M.; Karypis, G. & Kumar, V.
A Comparison of Document Clustering Techniques
2000

Inproceedings (pelleg99accelerating)
Pelleg, D. & Moore, A.
Accelerating Exact k -means Algorithms with Geometric Reasoning
1999, 277-281

Inproceedings (bradley98refining)
Bradley, P. S. & Fayyad, U. M.
Refining initial points for K-Means clustering
Morgan Kaufmann, San Francisco, CA, 1998, 91-99

Book (hartigan75)
Hartigan, J.
Clustering Algorithms
John Wiley and Sons, New York, 1975

Inproceedings (MacQueen67)
MacQueen, J. B.
Cam, L. M. L. & Neyman, J. (ed.)
Some Methods for Classification and Analysis of MultiVariate Observations
University of California Press, 1967, 1, 281-297

Techreport (ballhall65)
Ball, G. & Hall, D.
ISODATA: A novel method of data analysis and pattern classification
Stanford Research Institute, 1965

Article (forgy65)
Forgy, E.
Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification
Biometrics, 1965, 21, 768-769