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Speeding-up the kernel k-means clustering method: A prototype based hybrid approach

  • Published:2013-04 Issue:5 Volume:34 Page:564-573
  • ISSN:0167-8655
  • Container-title:Pattern Recognition Letters
  • language:en
  • Short-container-title:Pattern Recognition Letters

Author:

Sarma T. Hitendra,Viswanath P.,Reddy B. Eswara

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference34 articles.

1. An incremental data mining algorithm for compact realization of prototypes;Ananthanarayana;Pattern Recognition,2001

2. Comparison of genetic algorithms based prototype selection schemes;Babu;Pattern Recognition,2001

3. Convergence properties of the k-means algorithms;Bottou,1995

4. Comparison of performance between different SVM kernels for the identification of adult video;Bouirouga;World Acad. Sci. Eng. Technol.,2011

5. Approximate kernel k-means: solution to large scale kernel clustering;Chitta,2011

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gabriel graph-based connectivity and density for internal validity of clustering;Progress in Artificial Intelligence;2020-06-09

2. Hybrid Approach to Speed-Up the Privacy Preserving Kernel K-means Clustering and its Application in Social Distributed Environment;Journal of Network and Systems Management;2020-01-16

3. A Family of Unsupervised Sampling Algorithms;Sampling Techniques for Supervised or Unsupervised Tasks;2019-10-27

4. ProTraS: A probabilistic traversing sampling algorithm;Expert Systems with Applications;2018-09

5. A new clustering validity index for arbitrary shape of clusters;Pattern Recognition Letters;2018-09
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