A bad instance for k-means++

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6648 LNCS, Issue , 2011, Pages 344-352

  Brunsch, Tobias ^a   Röglin, Heiko ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION RATIOS; K-MEANS; K-MEANS METHOD; NUMBER OF CLUSTERS; SEEDING TECHNIQUES; UPPER BOUND;

PROBABILITY;

EID: 79955773511     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20877-5_34     Document Type: Conference Paper

References (8)

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2. Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of Euclidean sum-of-squares clustering. Machine Learning 75(2), 245-248 (2009)
3. Arthur, D., Manthey, B., Röglin, H.: k-means has polynomial smoothed complexity. In: Proc. of the 50th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 405-414 (2009)
4. Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proc. of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1027-1035. SIAM, Philadelphia (2007)
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7. Lloyd, S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129-136 (1982)
8. Vattani, A.: k-means requires exponentially many iterations even in the plane. In: Proc. of the 25th ACM Symposium on Computational Geometry (SoCG), pp. 324-332. ACM, New York (2009)