ASCCN: Arbitrary shaped clustering method with compatible nucleoids

International Journal of Data Warehousing and Mining

Volumn 6, Issue 4, 2010, Pages 1-15

  Wan, Renxia ^a   Wang, Lixin ^b   Su, Xiaoke ^a  

^a DONGHUA UNIVERSITY   (China)

^b Faculty of Civil Engineering   (China)

Author keywords

Cluster; Compatible; Data Mining; Dissimilarity; Grid

Indexed keywords

CLUSTER; CLUSTER GRIDS; CLUSTERING METHODS; COMPATIBLE; COMPUTATIONAL COSTS; DATA POINTS; DENSITY-BASED; DISSIMILARITY; DISSIMILARITY MEASURES; GRID; NUCLEOIDS; PARTITION METHODS;

DATA MINING;

CLUSTERING ALGORITHMS;

EID: 79953121004     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2010100101     Document Type: Article

References (32)

1. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). A framework for clustering evolving data streams. In Proceedings of 29th International Conference on Very Large Data Bases (pp. 81-92).
2. Agrawa, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM SIGMOD conference on Management of Data (SIGMOD'98) (pp. 94-105). (Pubitemid 128655960)
3. Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD'99) (pp. 49-60). (Pubitemid 129597323)
4. Bhatnagar, V., Kaur, S., & Mignet, L. (2009). A parameterized framework for clustering streams. International Journal of Data Warehousing and Mining, 5(1), 36-56.
5. Biçici, E., & Yuret, D. (2007). Locally scaled density based clustering. In Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'07) (pp. 739-748).
6. Bradley, P. S., Fayyad, U. M., & Reina, C. A. (1998). Scaling EM (Expectation Maximization) clustering to large database (Microsoft Research Rep. MSRTR- 98-35).
7. Cannon, R. L., Dave, J. V., & Bezdek, J. C. (1986). Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 248-255. doi:10.1109/TPAMI.1986. 4767778
8. Cao, F., Ester, M., Qian, W., & Zhou, A. (2006). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM Conference on Data Mining (SDM'06) (pp. 326-337).
9. Chen, H., Guo, G., Huang, Y., & Huang, T. (2008). A spatial overlapping based similarity measure applied to hierarchical clustering. In Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'08) (pp. 371-375).
10. Chen, Y., & Tu, L. (2007). Density-based clustering for real-time stream data. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133-142). (Pubitemid 350229200)
11. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial database with noise. In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96) (pp. 226-232).
12. Guha, S., Rastogi, R., & Shim, K. (1998). CURE: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD conference on Management of Data (SIGMOD'98) (pp. 73-84). (Pubitemid 128655958)
13. Halkidi, M., & Vazirgiannis, M. (2001). Clustering validity assessment: Finding the optimal partitioning of a data set. In Proceedings of International Conference on Data Mining.
14. Han, J., Kanber, M., & Tung, A. K. H. (2001). Spatial clustering methods in data mining: A survey. Geographic Data Mining and Knowledge Discovery. New York: Taylor and Francis.
15. Hinneburg, A., & Keim, D. A. (1998). An efficient approach to clustering in large Multimedia Database with noise. In Proceedings of 4th International Conference on Konwledge Discovery and Data Mining (KDD'98) (pp. 58-65).
16. Hore, P., Hall, L. O., & Goldgof, D. B. (2007). Single pass fuzzy c-means. IEEE International Fuzzy Systems Conference, 1-7.
17. Huang, Z. (1998). Extensions to the κ-Means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 283-304. doi:10.1023/A:1009769707641 (Pubitemid 128695480)
18. Kaufma, L., & Rousseeuw, P. J. (1990). Finding groups in data: an introduction to cluster analysis. New York: John Wiley & Sons.
19. Kwok, T., Smith, K. A., Lozano, S., & Taniar, D. (2002). Parallel Fuzzy c-Means Clustering for Large Data Sets. Euro-Par, 365-374.
20. Le-Khac, N., Aouad, L. M., & Kechadi, M. (2007). A new approach for distributed density based clustering on grid platform. In Proceedings of the 24th British national conference on databases (BNCOD'07) (pp. 247-258).
21. Li, M., Lee, G., Lee, W., & Sivasubramaniam, A. (2006). PENS: an algorithm for density-based clustering in peer-to-peer systems. In Proceedings of the 1st international conference on scalable information systems.
22. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281-297).
23. Ng, R. T., & Han, J. (1994). Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94) (pp. 144-155).
24. Pal, N. R., & Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3(3), 370-379. doi:10.1109/91.413225
25. Song, M., Hu, X., Yoo, I., & Koppel, E. (2009). A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature. International Journal of Data Warehousing and Mining, 5(4), 44-57.
26. Sun, H., Yu, G., Bao, Y., Zhao, F., & Wang, D. (2005). CDS-Tree: An effective index for clustering arbitrary shapes in data streams. In Proceedings of the 15th international workshop on research issues in data engineering: stream data mining and applications (RIDE-SDMA'05).
27. Tan, L., Taniar, D., & Smith, K. A. (2005). A clustering algorithm based on an estimated distribution model. International Journal of Data Warehousing and Mining, 1(2), 229-245.
28. Wan, R., Wang, L., & Su, X. (2009). PNMBG: Point neighborhood merging with border grids. Journal of Information and Organizational Sciences, 33(2), 297-305.
29. Wang, W., Yang, J., & Muntz, R. (1997). STING: A statistical information grid approach to spatial data mining. In Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB'97) (pp. 186-195).
30. Zhang, T., Ramakrishnam, R., & Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD'96) (pp. 103-114). (Pubitemid 126440760)
31. Zhang, X., Hu, X., Xia, J., Zhou, X., & Achananuparp, P. (2008). A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information. International Journal of Data Warehousing and Mining, 4(4), 84-101.
32. Zhang, X., Jing, L., Hu, X., Ng, M., Xia, J., & Zhou, X. (2008). Medical Document Clustering Using Ontology-Based Term Similarity Measures. International Journal of Data Warehousing and Mining, 4(1), 62-73.