Data field for hierarchical clustering

International Journal of Data Warehousing and Mining

Volumn 7, Issue 4, 2011, Pages 43-63

  Wang, Shuliang ^a,b   Wenyan Gan, ^c   Li, Deyi ^d   Li, Deren ^b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b WUHAN UNIVERSITY   (China)

^c NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d TSINGHUA UNIVERSITY   (China)

Author keywords

Core objects; Data field; Data mining; Hierarchical clustering; Interaction among objects

Indexed keywords

CLUSTERING PROCESS; CORE OBJECTS; DATA FIELDS; DATA OBJECTS; DATA SPACE; EQUIPOTENTIAL LINES; HIER-ARCHICAL CLUSTERING; IMPACT FACTOR; INTERACTION AMONG OBJECTS; K-MEANS; MUTUAL INTERACTION; NOISY DATA; NUCLEAR FIELDS; RANDOM SAMPLE; SELF-ORGANIZED PROCESS;

DATA MINING; HARDWARE; SOFTWARE ENGINEERING;

CLUSTERING ALGORITHMS;

EID: 84255210434     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2011100103     Document Type: Article

References (32)

1. Bhatnagar, V., Kaur, S., & Mignet, L. (2009). A parameterized framework for clustering streams. International Journal of Data Warehousing and Mining, 5(1), 36-56. doi:10.4018/jdwm.2009010103
2. Blatt, F. J. (1986). Principles of physics (2nd ed.). Boston, MA: Allyn and Bacon.
3. Engle, K., & Gangopadhyay, A. (2010). An efficient method for discretizing continuous attributes. International Journal of Data Warehousing and Mining, 6(2), 1-21. doi:10.4018/jdwm.2010040101
4. George, K., Han, E. H., & Kumar, V. (1999). CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 27(3), 329-341.
5. Giachetta, G., Mangiarotti, L., & Sardanashvily, G. (2009). Advanced classical field theory. Singapore: World Scientific. doi:10.1142/9789812838964
6. Grabmeier, J., & Rudolph, A. (2002). Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery, 6, 303-360. doi:10.1023/A:1016308404627 (Pubitemid 37113862)
7. Guha, S., Rastogi, R., & Shim, K. (1998). CURE: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp.73-84). New York, NY: ACM Press. (Pubitemid 128655958)
8. Heller, K. A., & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (pp. 297-304). New York, NY: ACM Press. (Pubitemid 43183346)
9. Horng, S. J., Su, M. Y., Chen, Y. H., Kao, T. W., Chen, R. J., Lai, J. L., & Perkasa, C. D. (2011). A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert Systems with Applications, 38(1), 306-313. doi:10.1016/j.eswa.2010.06.066
10. Kim, S., Shephard, N., & Chib, S. (1998). Stochastic volatility: Likelihood inference and comparison with ARCH model. The Review of Economic Studies, 65, 361-393. doi:10.1111/1467-937X.00050 (Pubitemid 128505119)
11. Kwok, T., Smith, K. A., Sebastián, L., & Taniar, D. (2002). Parallel fuzzy c-means clustering for large data sets. In B. Monien & R. Feldmann (Eds.), Proceedings of the 8th International Euro-Par Conference (LNCS, 2400, pp. 365-374).
12. Labini, F. S. (2008). Gravitational clustering: An overview (Tech. Rep. No. arXiv 0806.2560). Retrieved from http://arxiv.org/abs/0806.2560
13. Lam, K. M., & Yan, H. (1995). Locating and extracting the eye in human face images. Pattern Recognition, 29, 771-779. (Pubitemid 126376471)
14. Li, D. R., Wang, S. L., & Li, D. Y. (2006). Spatial data mining theories and applications (pp. 171-177). Beijing, China: Science Press.
15. Li, J., Wang, K., & Xu, L. (2008). Chameleon based on clustering feature tree and its application in customer segmentation. Annals of Operations Research, 168(1), 225-245. doi:10.1007/s10479-008-0368-4
16. Li, X., Zhang, S. C., & Wang, S. L. (2006). Advances in data mining applications. International Journal of Data Warehousing and Mining, 2(3), i-iii.
17. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA (Vol. 1, pp. 281-297).
18. Malik, H. H., Kender, J. R., Fradkin, D., & Moerchen, F. (2010). Hierarchical document clustering using local patterns. Data Mining and Knowledge Discovery, 21, 153-185. doi:10.1007/s10618-010-0172-z
19. Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms. The Computer Journal, 26(4), 354-359. (Pubitemid 14590891)
20. Parsons, L., Haque, E., & Liu, H. (2004). Subspace clustering for high dimensional data: A review. SIGKDD Explorations, 6(1), 90-105. doi:10.1145/1007730.1007731
21. Sander, J., Ester, M., Kriegel, H. P., & Xu, X. W. (1998). Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2, 169-194. doi:10.1023/A:1009745219419 (Pubitemid 128126770)
22. Schölkop, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443-1471. doi:10.1162/ 089976601750264965 (Pubitemid 33595028)
23. Sembiring, R. W., Zain, J. M., & Embong, A. (2010). A comparative agglomerative hierarchical clustering method to cluster implemented course. Journal of Computing, 2(12), 1-6.
24. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423, 623-656.
25. Silla, C. N. Jr, & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22, 31-72. doi:10.1007/s10618-010-0175-9
26. Song, M., Hu, X. H., 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. doi:10.4018/jdwm.2009080703
27. Wan, R. X., Wang, L. X., & Su, X. K. (2010). ASCCN: Arbitrary shaped clustering method with compatible nucleoids. International Journal of Data Warehousing and Mining, 6(4), 1-15. doi:10.4018/jdwm.2010100101
28. Wang, S. L., & Wang, X. Z. (2007). A fuzzy comprehensive clustering method. In R. Alhajj, H. Gao, X. Li, J. Li, & O. R. Zaïane (Eds.), Proceedings of the Third International Conference on Advanced Data Mining and Applications (LNCS 4632, pp. 488-499).
29. Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678. doi:10.1109/TNN.2005.845141 (Pubitemid 40718010)
30. Zhang, T., Ramakrishnman, R., & Linvy, M. (1996). BIRCH: An efficient method for very large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data (pp.103-114). New York, NY: ACM Press. (Pubitemid 126440760)
31. Zhang, X. D., Jing, L. P., Hu, X. H., Ng, M., Xia, J. L., & Zhou, X. H. (2008). Medical document clustering using ontology-based term similarity measures. International Journal of Data Warehousing and Mining, 4(1), 62-73. doi:10.4018/jdwm.2008010104
32. Zhao, Y., & Karypis, G. (2005). Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery, 10(2), 141-168. doi:10.1007/s10618-005-0361-3 (Pubitemid 41217849)