A Fuzzy Subspace Algorithm for Clustering High Dimensional Data

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

   Article  Google Scholar 

2. Cao, Y., Wu, J.: Projective ART for clustering data sets in high dimensional spaces. Neural Networks 15, 105–120 (2002)

   Article  Google Scholar 

3. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD Record ACM Special Interest Group on Management of Data, pp. 94–105 (1998)

   Google Scholar 

4. Aggarwal, C., Wolf, J., Yu, P., Procopiuc, C., Park, J.: Fast algorithms for projected clustering. In: Proceedings of the 1999 ACM SIGMOD international conference on Management of data, pp. 61–72. ACM Press, New York (1999)

   Chapter  Google Scholar 

5. Domeniconi, C., Papadopoulos, D., Gunopulos, D., Ma, S.: Subspace clustering of high dimensonal data. In: Proceedings of the SIAM International Conference on Data Mining, Lake Buena Vista, Florida (2004)

   Google Scholar 

6. Goil, S., Nagesh, H., Choudhary, A.: MAFIA: Efficient and scalable subspace clustering for very large data sets. Technical Report CPDC-TR-9906-010, Center for Parallel and Distributed Computing, Department of Electrical & Computer Engineering, Northwestern University (1999)

   Google Scholar 

7. Aggarwal, C., Yu, P.: Finding generalized projected clusters in high dimensional spaces. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, May 16-18, 2000, vol. 29, pp. 70–81. ACM, New York (2000)

   Chapter  Google Scholar 

8. Woo, K., Lee, J.: FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting. PhD thesis, Korea Advanced Institue of Science and Technology, Department of Electrical Engineering and Computer Science (2002)

   Google Scholar 

9. Cheng, C., Fu, A., Zhang, Y.: Entropy-based subspace clustering for mining numerical data. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 84–93. ACM Press, New York (1999)

   Chapter  Google Scholar 

10. Kaufman, L., Rousseeuw, P.: Finding Groups in Data–An Introduction to Cluster Analysis. Wiley series in probability and mathematical statistics. John Wiley & Sons, Inc., New York (1990)

    Google Scholar 

11. Yang, J., Wang, W., Wang, H., Yu, P.: δ-clusters: capturing subspace correlation in a large data set. In: Proceedings. 18th International Conference on Data Engineering, pp. 517–528 (2002)

    Google Scholar 

12. Procopiuc, C., Jones, M., Agarwal, P., Murali, T.: A monte carlo algorithm for fast projective clustering. In: Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pp. 418–427. ACM Press, New York (2002)

    Chapter  Google Scholar 

13. Gan, G., Wu, J.: Subspace clustering for high dimensional categorical data. ACM SIGKDD Explorations Newsletter 6, 87–94 (2004)

    Article  Google Scholar 

14. Agarwal, P., Mustafa, N.: k-means projective clustering. In: Proceedings of the Twenty-third ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems(PODS), Paris, France, pp. 155–165. ACM Press, New York (2004)

    Chapter  Google Scholar 

15. Liu, B., Xia, Y., Yu, P.: Clustering through decision tree construction. In: Proceedings of the ninth international conference on Information and knowledge management, McLean, Virginia, USA, pp. 20–29. ACM Press, New York (2000)

    Google Scholar 

16. Hartigan, J.: Clustering Algorithms. John Wiley & Sons, Toronto (1975)

    MATH  Google Scholar 

17. Huang, Z., Ng, M.: A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems 7, 446–452 (1999)

    Article  Google Scholar 

Download references