1. Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)

   Article  Google Scholar 

2. Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall (2008)

   Google Scholar 

3. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press (1967)

   Google Scholar 

4. Sweeney, L.: Uniqueness of simple demographics in the U.S. population (2000),http://privacy.cs.cmu.edu/

5. Aggarwal, C., Yu, P.: Privacy-Preserving Data Mining: Models and Algorithms. Springer (2008)

   Google Scholar 

6. Froomkin, A.: The death of privacy? Stanford Law Review 52 (2000)

   Google Scholar 

7. Sweeney, L.: k-anonymity: A model for protecting privacy. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 10(5) (2002)

   Google Scholar 

8. Meyerson, A., Williams, R.: On the complexity of optimal K-anonymity. In: Proceedings of PODS 2004. ACM (2004)

   Google Scholar 

9. Yu, T., Jajodia, S. (eds.): Secure Data Management in Decentralized Systems, vol. 33. Springer (2007)

   Google Scholar 

10. Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving anonymity via clustering. In: Proceedings of PODS 2006. ACM (2006)

    Google Scholar 

11. Wieland, S., Cassa, C., Mandl, K., Berger, B.: Revealing the spatial distribution of a disease while preserving privacy. Proc. Natl. Acad. Sci. USA 105(46) (2008)

    Google Scholar 

12. Li, W.:r-anonymized clustering. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part I. LNCS, vol. 7663, pp. 455–464. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

13. Peng, J., Wei, Y.: Approximating k-means-type clustering via semidefinite programming. SIAM Journal on Optimization 18(1) (2007)

    Google Scholar 

14. Lisser, A., Rendl, F.: Graph partitioning using linear and semidefinite programming. Mathematical Programming 95(1) (2003)

    Google Scholar 

15. Riedel, S., Clarke, J.: Incremental integer linear programming for non-projective dependency parsing. In: Proceedings of EMNLP 2006 (2006)

    Google Scholar 

16. Xing, E., Jordan, M.: On semidefinite relaxation for normalized k-cut and connections to spectral clustering. Technical report, University of California, Berkeley (2003)

    Google Scholar 

17. Li, W., Schuurmans, D.: Modular community detection in networks. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1366–1371. AAAI (2011)

    Google Scholar 

18. Li, W.: Modularity segmentation. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 100–107. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

19. Asuncion, A., Newman, D.: UCI machine learning repository (2007)

    Google Scholar 

20. Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315 (2007)

    Google Scholar 

21. Li, W., Xu, L., Schuurmans, D.: Facility locations revisited: An efficient belief propagation approach. In: 2010 IEEE International Conference on Automation and Logistics, pp. 408–413. IEEE (2010)

    Google Scholar 

22. Li, W.: Clustering with uncertainties: An affinity propagation-based approach. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part V. LNCS, vol. 7667, pp. 437–446. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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