Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm-Reference-Cited by-同舟云学术

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Published:2014-10-17 Issue: Volume: Page:79-98
ISSN:
Container-title:Partitional Clustering Algorithms
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Author:

Celebi M. Emre,Kingravi Hassan A.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-09259-1_3

Reference112 articles.

1. Al-Daoud M (2005) A new algorithm for cluster initialization. In: Proceedings of the 2nd world enformatika conference, pp 74–76

2. Al Hasan M, Chaoji V, Salem S, Zaki M (2009) Robust partitional clustering by outlier and density insensitive seeding. Pattern Recognit Lett 30(11):994–1002

3. Al-Sultan KS (1995) A Tabu search approach for the minimum sum-of-squares clustering problem. Pattern Recognit 28(9):1443–1451

4. Aloise D, Deshpande A, Hansen P, Popat P (2009) NP-hardness of Euclidean sum-of-squares clustering. Mach Learn 75(2):245–248

5. An F, Mattausch HJ (2013) K-means clustering algorithm for multimedia applications with flexible HW/SW co-design. J Syst Archit 59(3):155–164

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