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The Research on Large Scale Data Set Clustering Algorithm Based on Tag Set

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Book cover Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

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Abstract

This paper proposes a set of SSLOKmeans algorithm that helps to guide the clustering before using tag memory resident, this algorithm can further improve the large-scale data sets clustering efficiency and clustering results of quality.

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References

  1. Kantabutra, S., Couch, A.L.: Parallel K-means clustering algorithm on NOWs. Tech. J. 1(6), 243–248 (2000)

    Google Scholar 

  2. Alsabti, K., Ranka, S., Singh, V.: An efficient K-means clustering algorithm. In: Proceedings of IPPS/SPDP Workshop on High Performance Data Mining, 1998, pp. 1–6. ACM, New York, NY (2011)

    Google Scholar 

  3. Chang, H., Yeung, D.Y.: Locally linear metric adaptation with application to semi-supervised clustering and image retrieval. Pattern Recogn. 39(7), 1253–1264 (2006)

    Article  MATH  Google Scholar 

  4. Wagstaff, K., Cardie, C., Rogers, S., et al.: Constrained K-means clustering with background knowledge. In: Proceedings of the 18th International Conference on Machine Learning, pp. 577–584. Morgan Kaufmann, San Francisco, CA (2001)

    Google Scholar 

  5. Basu, S., Banerjee, A., Mooney. R.: Semi-supervised clustering by seeding. In: Proceedings of the 19th International Conference on Machine Learning, pp. 27–34. Morgan Kaufmann, San Francisco, CA (2002)

    Google Scholar 

  6. Basu, S., Bilenko, M., Mooney R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59–68. ACM, New York, NY (2004)

    Google Scholar 

  7. Lu, Z., Leen, T.K.: Semi-supervised clustering with pair wise constraints: a discriminative approach. In: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007, pp. 299–306. Microtome Publishing, USA (2007)

    Google Scholar 

  8. Bradley, P.S., Fayyad, U., Reina, C.: Scaling clustering algorithms to large databases. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 9–15. AAAI Press, New York, USA (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Guangdong University of Science and Technology, Dongguan, 523083, Guangdong Province, China

    Qiang Chen

Authors
  1. Qiang Chen
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Corresponding author

Correspondence to Qiang Chen .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Chen, Q. (2016). The Research on Large Scale Data Set Clustering Algorithm Based on Tag Set. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_38

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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