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Impact of Learners’ Quality and Diversity in Collaborative Clustering Cover

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Journal of Artificial Intelligence and Soft Computing Research
Volume 9 (2019): Issue 2 (April 2019)
By: Parisa Rastin,  Basarab Matei,  Guénaël Cabanes,  Nistor Grozavu and  Younès Bennani  
Open Access
|Dec 2018

References

  1. [1] R. E. Schapire, The strength of weak learn-ability, Mach. Learn., vol. 5, no. 2, pp. 197–227, Jul. 1990. [Online]. Available: http://dx.doi.org/10.1023/A:102264880076010.1023/A:1022648800760
    Open DOISearch in Google ScholarBack to article
  2. [2] D. H. Wolpert, Stacked generalization, Neural Networks, vol. 5, pp. 241–259, 199210.1016/S0893-6080(05)80023-1
    Search in Google ScholarBack to article
  3. [3] J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas, On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226–239, Mar. 1998. [Online]. Available: http://dx.doi.org/10.1109/34.66788110.1109/34.667881
    Open DOISearch in Google ScholarBack to article
  4. [4] P. Bachman, O. Alsharif, and D. Precup, Learning with pseudo-ensembles, in Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 3365–3373
    Search in Google ScholarBack to article
  5. [5] A. Strehl and J. Ghosh, Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions, Journal on Machine Learning Research (JMLR), vol. 3, pp. 583–617, Dec. 2002
    Search in Google ScholarBack to article
  6. [6] J. da Silva and M. Klusch, Inference on distributed data clustering, in Machine Learning and Data Mining in Pattern Recognition, ser. Lecture Notes in Computer Science, P. Perner and A. Imiya, Eds. Springer Berlin Heidelberg, 2005, vol. 3587, pp. 610–619. [Online]. Available: http://dx.doi.org/10.1007/11510888_6010.1007/11510888_60
    Open DOISearch in Google ScholarBack to article
  7. [7] W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognition Letters, vol. 23, no. 14, pp. 1675–1686, 200210.1016/S0167-8655(02)00130-7
    Search in Google ScholarBack to article
  8. [8] N. Grozavu, M. Ghassany, and Y. Bennani, Learning confidence exchange in collaborative clustering, in IJCNN, 2011, pp. 872–87910.1109/IJCNN.2011.6033313
    Search in Google ScholarBack to article
  9. [9] W. Pedrycz and K. Hirota, A consensus-driven fuzzy clustering, Pattern Recogn. Lett., vol. 29, no. 9, pp. 1333–1343, 200810.1016/j.patrec.2008.02.015
    Search in Google ScholarBack to article
  10. [10] N. Grozavu, G. Cabanes, and Y. Bennani, Diversity analysis in collaborative clustering, in IEEE World Congress on Computational Intelligence, 201410.1109/IJCNN.2014.6889528
    Search in Google ScholarBack to article
  11. [11] B. Depaire, R. Falcón, K. Vanhoof, and G. Wets, Pso driven collaborative clustering: A clustering algorithm for ubiquitous environments, Intell. Data Anal., vol. 15, no. 1, pp. 49–68, Jan. 2011. [Online]. Available: http://dl.acm.org/citation.cfm?id=1937721.193772510.3233/IDA-2010-0455
    Search in Google ScholarBack to article
  12. [12] M. Ghassany, N. Grozavu, and Y. Bennani, Collaborative clustering using prototype-based techniques, International Journal of Computational Intelligence and Applications, vol. 11, no. 03, p. 1250017, 201210.1142/S1469026812500174
    Search in Google ScholarBack to article
  13. [13] S. Zhang, C. Zhang, and X. Wu, Knowledge Discovery in Multiple Databases, ser. Advanced Information and Knowledge Processing. Springer, 2004. [Online]. Available: http://dx.doi.org/10.1007/978-0-85729-388-610.1007/978-0-85729-388-6
    Open DOISearch in Google ScholarBack to article
  14. [14] W. Pedrycz, Interpretation of clusters in the framework of shadowed sets, Pattern Recogn. Lett., vol. 26, no. 15, pp. 2439–2449, 200510.1016/j.patrec.2005.05.001
    Search in Google ScholarBack to article
  15. [15] N. Grozavu and Y. Bennani, Topological collaborative clustering, Australian Journal of Intelligent Information Processing Systems, vol. 12, no. 3, 2010
    Search in Google ScholarBack to article
  16. [16] M. Ghassany, N. Grozavu, and Y. Bennani, Collaborative clustering using prototype-based techniques, International Journal of Computational Intelligence and Applications, vol. 11, no. 3, 201210.1142/S1469026812500174
    Search in Google ScholarBack to article
  17. [17] N. Grozavu and Y. Bennani, Topological Collaborative Clustering, in LNCS Springer of ICONIP’10 : 17th International Conference on Neural Information Processing, 2010
    Search in Google ScholarBack to article
  18. [18] T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cyb., vol. 43, pp. 59–69, 198210.1007/BF00337288
    Search in Google ScholarBack to article
  19. [19] Analysis of a simple self-organizing process, Biol. Cyb., vol. 44, pp. 135–140, 198210.1007/BF00317973
    Search in Google ScholarBack to article
  20. [20] C. M. Bishop and C. K. I. Williams, GTM: The generative topographic mapping, Neural Computation, vol. 10, pp. 215–234, 199810.1162/089976698300017953
    Search in Google ScholarBack to article
  21. [21] N. Grozavu, Y. Bennani, and M. Lebbah, From variable weighting to cluster characterization in topographic unsupervised learning, in Proc. of IJCNN09, International Joint Conference on Neural Network, 200910.1109/IJCNN.2009.5178666
    Search in Google ScholarBack to article
  22. [22] N. Grozavu and Y. Bennani, Topological collaborative clustering, Australian Journal of Intelligent Information Processing Systems, vol. 12, no. 2, 2010
    Search in Google ScholarBack to article
  23. [23] J. Sublime, N. Grozavu, G. Cabanes, Y. Bennani, and A. Cornuéjols, From horizontal to vertical collaborative clustering using generative topographic maps, International Journal of Hybrid Intelligent Systems, vol. 12, no. 4, 201610.3233/HIS-160219
    Search in Google ScholarBack to article
  24. [24] L. I. Kuncheva and C. J. Whitaker, Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy, Mach. Learn., vol. 51, no. 2, pp. 181–207, May 200310.1023/A:1022859003006
    Search in Google ScholarBack to article
  25. [25] F. Gullo, A. Tagarelli, and S. Greco, Diversity-Based Weighting Schemes for Clustering Ensembles, in SDM, 2009, pp. 437–44810.1137/1.9781611972795.38
    Search in Google ScholarBack to article
  26. [26] N. Grozavu, M. Ghassany, and Y. Bennani, Learning confidence exchange in collaborative clustering, in Neural Networks (IJCNN), The 2011 International Joint Conference on IEEE, 2011, pp. 872–87910.1109/IJCNN.2011.6033313
    Search in Google ScholarBack to article
  27. [27] A. K. Jain and R. C. Dubes, Algorithms for clustering data. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1988
    Search in Google ScholarBack to article
  28. [28] W. M. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association, vol. 66, no. 336, pp. 846–850, Dec. 197110.1080/01621459.1971.10482356
    Search in Google ScholarBack to article
  29. [29] L. Hubert and P. Arabie, Comparing Partitions, Journal of the Classification, vol. 2, pp. 193–218, 198510.1007/BF01908075
    Search in Google ScholarBack to article
  30. [30] P. Jaccard, The distribution of the flora in the alpine zone, New Phytologist, vol. 11, no. 2, pp. 37–50, 191210.1111/j.1469-8137.1912.tb05611.x
    Search in Google ScholarBack to article
  31. [31] D. L. Wallace, A Method for Comparing Two Hierarchical Clusterings: Comment, Journal of the American Statistical Association, vol. 78, no. 383, pp. pp. 569–576, 1983. [Online]. Available: http://www.jstor.org/stable/228811810.2307/2288118
    Search in Google ScholarBack to article
  32. [32] F. Pinto, J. Carrico, M. Ramirez, and J. Almeida, Ranked Adjusted Rand: integrating distance and partition information in a measure of clustering agreement, BMC Bioinformatics, vol. 8, no. 1, p. 44, 2007. [Online]. Available: http://www.biomedcentral.com/1471-2105/8/4410.1186/1471-2105-8-44180209317286861
    Search in Google ScholarBack to article
  33. [33] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005
    Search in Google ScholarBack to article
  34. [34] M. Meila, Comparing clusterings - an information based distance, Journal of Multivariate Analysis, vol. 98, pp. 873–895, 200710.1016/j.jmva.2006.11.013
    Search in Google ScholarBack to article
  35. [35] A. Frank and A. Asuncion, UCI machine learning repository, 2010. [Online]. Available: http://archive.ics.uci.edu/ml
    Search in Google ScholarBack to article
  36. [36] T. Calinski and J. Harabasz, Dendrite method for cluster analysis, Communications in Statistics, vol. 3, no. 1, pp. 1–27, 197410.1080/03610917408548446
    Search in Google ScholarBack to article
  37. [37] D. L. Davies and D. W. Bouldin, A cluster separation measure, IEEE Trans. Pattern Anal. Mach. Intell., vol. 1, no. 2, pp. 224–227, Feb. 197910.1109/TPAMI.1979.4766909
    Search in Google ScholarBack to article
  38. [38] W. J. Krzanowski and Y. T. Lai, A criterion for determining the number of groups in a data set using sum-of-squares clustering, Biometrics, vol. 44, no. 1, pp. pp. 23–34, 1988. [Online]. Available: http://www.jstor.org/stable/253189310.2307/2531893
    Search in Google ScholarBack to article
  39. [39] P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol. 20, no. 0, pp. 53 – 65, 1987. [Online]. Available: http://www.sciencedirect.com/science/article/pii/037704278790125710.1016/0377-0427(87)90125-7
    Search in Google ScholarBack to article
  40. [40] K. Kiviluoto, Topology Preservation in Self-Organizing Maps, International Conference on Neural Networks, pp. 294–299, 199610.1109/ICNN.1996.548907
    Search in Google ScholarBack to article
  41. [41] T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 200110.1007/978-3-642-56927-2
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jaiscr-2018-0030
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Language: English
Page range: 149 - 165
Submitted on: Jan 28, 2018
|
Accepted on: Jul 3, 2018
|
Published on: Dec 31, 2018
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
collaborative clustering,
topological neural networks,
unsupervised learning,
diversity,
quality
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2018 Parisa Rastin, Basarab Matei, Guénaël Cabanes, Nistor Grozavu, Younès Bennani, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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