An improved cop-kmeans clustering for solving constraint violation based on mapreduce framework

Fundamenta Informaticae

Volumn 126, Issue 4, 2013, Pages 301-318

  Yang, Yan ^a   Rutayisire, Tonny ^a   Lin, Chao ^a   Li, Tianrui ^a   Teng, Fei ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Constraint violation; Cop Kmeans; MapReduce; Pairwise constraints; Semi supervised clustering

Indexed keywords

CONSTRAINT VIOLATION; COP-KMEANS; MAP-REDUCE; PAIRWISE CONSTRAINTS; SEMI-SUPERVISED CLUSTERING;

METADATA;

CLUSTERING ALGORITHMS;

EID: 84887879508     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: 10.3233/FI-2013-883     Document Type: Article

References (31)

1. Davidson, I., Basu, S.:A survey of clustering with instance level constraints, ACM Transactions on Knowledge Discovery on Data, 2007, 1-41.
2. Wang G., Wang Y.: 3DM: Domain-oriented Data-driven Data Mining, Fundamenta Informaticae, 90, 2009, 395-426.
3. Yu J., Yang M., Hao P.: A Novel Multimodal Probability Model for Cluster Analysis, Fundamenta Informaticae, 111, 2011, 81-90.
4. Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means clustering with background knowledge, Proc. International Conference on Machine Learning, 2001, 577-584.
5. Basu S., Banerjee A., Mooney R.J.: Semi-Supervised clustering by seeding, Proc. 19th International Conference on Machine Learning, 2002, 19-26.
6. Xing E.P., Ng A.Y., Jordan M.I., et al.: Distance metric learning, with application to clustering with sideinformation, Advances in Neural Information Processing Systems, 2003, 505-512.
7. Saha. S., Bandyopadhyay S.: Semi-GAPS: A Semi-supervised Clustering Method, Fundamenta Informaticae, 96, 2009, 195C209.
8. Wagstaff, K., Cardie, C.: Clustering with instance level constraints, Proc. International Conference on Machine Learning, 2000, 1103-1110.
9. Davidson, I., Ravi, S.S.: Clustering with constraints: Feasibility issues and the k-means algorithm, Proc. SIAM International Conference on Data Mining, 2005, 138-149.
10. Sun Y., Liu M., Wu C.: A Modified K-means Algorithm for Clustering Problem with Balancing Constraints, Proc. 3rd International Conference on Measuring Technology and Mechatronics Automation, 2011, 127-130.
11. Schmidt J., Brandle E. M., Kramer S.: Clustering with Attribute-Level Constraints, Proc. 11th IEEE International Conference on Data Mining, 2011, 1206-1211.
12. Basu, S., Banerjee, A., Mooney, R.J.: Active semi-supervision for pairwise constrained clustering, Proc. SIAM International Conference on Data Mining, 2004, 333-344.
13. Wagstaff, K.: Intelligent clustering with instance-level constraints, Cornell University, 2002.
14. Tan, W., Yang, Y., Li, T.: An improved COP-KMeans algorithm for solving constraint violation, Proc. International FLINS Conference on Foundations and Applications of Computational Intelligence, 2010, 690-696.
15. Anthony, K., Han, J., Raymond, T.: Constraint-Based Clustering in Large Databases, Proc. International Conference on Database Theory, 2001, 405-419.
16. Rutayisire T., Yang Y., Lin C., Zhang J.: A Modified Cop-Kmeans Algorithm Based on Sequenced Cannot-Link Set, Proc. 6th International Conference on Rough Sets and Knowledge Technology (RSKT2011), LNAI 6954, Springer, 2011, 217-225.
17. Dean J., Ghemawat S.: MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM, 51(1), 2008, 107-113.
18. Xuan, W.: Clustering in the Cloud: Clustering Algorithm Adoption to Hadoop Map/Reduce Framework, Technical Reports-Computer Science 19, 2010.
19. Yang Y., Chen Z.: Parallelized Computing of Attribute Core Based on Rough Set Theory and MapReduce, Proc. 7th International Conference on Rough Sets and Knowledge Technology (RSKT2012), LNAI 7414, Springer, 2012, 167-172.
20. Malay, K.: Clustering Large Databases in Distributed Environment, Proc. International Advanced Computing Conference, 2009, 351-358.
21. Zhang, Y., Xiong, Z., Mao, J.: The Study of Parallel K-Means Algorithm, Proc.World Congress on Intelligent Control and Automation, 2006, 5868-5871.
22. Zhao, W., Ma, H., He, Q.: Parallel K-Means Clustering Based on MapReduce, Proc. CloudCom, LNCS5931, 2009, 674-679.
23. Li H., Wu G., et al.: K-Means Clustering with Bagging and MapReduce, Proc. 44th Hawaii International Conference on System Sciences, 2011.
24. Lin C., Yang Y., Rutayisire T.: A Parallel Cop-Kmeans Clustering Algorithm Based on MapReduce Framework, Proc. 6th International Conference on Intelligent Systems & Knowledge Engineering, 2011, 93-102.
25. Haichao, H., Yong, C., Ruilian, Z.: A semi-supervised clustering algorithm based on must-link set, Proc. International conference on Advanced Data Mining & Applications, 2008, 492-499.
26. Davidson, I., Ravi, S.S.: Identifying and Generating easy sets of constraints for clustering, Proc. American Association for Artificial Intelligence, 2006, 336-341.
27. West, D.B.: Introduction to Graph Theory, Prentice Hall, Inc., Englewood Cliffs, NJ, 2001.
28. Wbite T.: Hadoop: The Definitive Guide, O'Reilly Media, Inc., Second Edition, 2011.
29. Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html
30. Yang Y., Kamel M.: An Aggregated Clustering Approach Using Multi-Ant Colonies Algorithms. Pattern Recognition, 39(7), 2006, 1278-1289.
31. Xu, X., Jager, J., Kriegel, H.P.: A Fast Parallel Clustering Algorithm for Large Spatial Databases. Data Mining and Knowledge Discovery, 3, 1999, 263-290.