Open Access
Issue
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
Article Number 01019
Number of page(s) 5
Section Intelligent Design and Computer Technology
DOI https://doi.org/10.1051/matecconf/201817601019
Published online 02 July 2018
  1. Jain, A.K., “Data clustering: 50 years beyond K-means” Pattern Recongnition letters 31, pp. 651-666 (2010) [CrossRef] [Google Scholar]
  2. Xu, R. and Wunsch, D., “Survey of Clustering Algorithms”, IEEE Transactions on Neural Networks, Vol.16, No.3, pp: 645-677 (2005) [Google Scholar]
  3. Rodriguez, A. and Lai, A., “Clustering by fast search and find of density peaks”, Science Vol 344 Issue 6191, pp. 1492-1496 (2014) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  4. Schwammle V. and Jensen O. N., “A simple and fast method to determine the parameters for fuzzy c-means cluster analysis”, Bioinformatics, Vol. 26, No.22, pp: 2841-2848 (2011) [CrossRef] [Google Scholar]
  5. Tzortzis, C., and Likas, A., “The Min-Max k-Means clustering algorithm”, Pattern Recognition, Vol. 47, pp. 2505-2516 (2014) [CrossRef] [Google Scholar]
  6. Ball, G and Hall, D., “ISODATA, a novel method of data analysis and pattern classification.”, Technical report NTISAD 699616. Stanford Research Institute, Stanford, CA, 1965. [Google Scholar]
  7. Kleinberg, J. “An impossibility theorem for clustering”, In: NIPS 15, pp. 463-470. (2002) [Google Scholar]
  8. Wallace, C.S and Freeman, P.R., “Estimation and Inference by compact coding” JRSSB, 49,, pp.240-251 (1987) [Google Scholar]
  9. Fukuyama, Y. and Sugeno, M. “A new method of choosing the number of clusters for the fuzzy c-means method.” Proc. 5th fuzzy syst. Symp., PP. 247(1989) [Google Scholar]
  10. MacQeen, J., “Some methods for classification and analysis of multivariated observations” In: Fifth Berkeley Symposium on Mathematics, Statistics and Probability. University of California Press, pp: 281297 (1965) [Google Scholar]
  11. Kaufman, L. and Rousseeuw, P.J. “Finding groups in data: an introduction to cluster analysis.” Wiley series in Probability and Statistics (2005) [Google Scholar]
  12. Mao, J. and Jain, A.K., “A self-organizing network for hyper-ellipsoidal clustering (HEC)”, IEEE Transactions on Neural Networks, Vol. 7, pp. 16-29 (1996) [CrossRef] [Google Scholar]
  13. Figureueredo, M. and Jain, A.K., “Unsupervised learning of finite mixture models” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 24, No. 3, pp. 381-396 (2002) [CrossRef] [Google Scholar]
  14. Tavazoie, S., Hughes, J.D., Compbell, m.J., Cho, R.J. and Church, G.M., “Systematics determination of genetic network architecture”, Nature Genetics, Vol. 22, 281-285 (1999) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.