MEC - Monitoring clusters' transitions

Frontiers in Artificial Intelligence and Applications

Volumn 222, Issue , 2010, Pages 212-224

  Oliveira, Márcia ^a   Gama, João ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Change Mining; Clusters; Evolution; Monitoring; Transitions

Indexed keywords

CHANGE MINING; CLUSTER; CLUSTER FRAMEWORK; CLUSTER MONITORING; CLUSTER STRUCTURE; CLUSTER TRANSITIONS; EVOLUTION; RESEARCH ISSUES; TRACKING MECHANISM; TRANSITION;

ARTIFICIAL INTELLIGENCE;

EID: 78650345243     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-60750-676-8-212     Document Type: Conference Paper

References (25)

1. Bottcher, M., Hoppner, F., Spiliopoulou, M. : On exploiting the power of time in data mining. SIGKDD Explorations (10), 3-11 (2008)
2. Jain, A. K. : Data Clustering: 50 Years Beyond K-means. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML/PKDD 2008. LNCS, vol. 5211. Springer, Belgium (2008)
3. Jain, A. K., Murty, M. N., Flynn, P. J. : Data Clustering: A Review. ACM Comput. Surv. (31), 264-323 (1999)
4. Ganti, V., Gehrke, J., Ramakrishnan, R. : A Framework for Measuring Changes in Data Characteristics. In: Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 126-137. ACM Press, Pennsylvania (1999)
5. Bartolini, I., Ciaccia, P., Ntoutsi, I., Patella, M., Theodoridis, Y. : The Panda framework for Comparing Patterns. Data Knowl. Eng. (68), 244-260 (2009)
6. Chawathe, S. S., Garcia-Molina, H. : Meaningful Change Detection in Structured Data. In: Peckham, J. (ed.) Proceedings ACM SIGMOD International Conference on Management of Data, pp. 26-37. ACM Press, Arizona (1997)
7. Oliveira, M., Gama, J.: Bipartite Graphs for Monitoring Clusters Transitions. In: Proceedings of the Ninth International Symposium on Intelligent Data Analysis. LNCS, vol. 6065, pp. to appear. Springer, Arizona (2010)
8. Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: MONIC: modeling and monitoring cluster transitions. In: Eliassi-Rad, T., Ungar, L. H., Craven, M., Gunopulos, D. (eds.) ACM SIGKDD 2006, pp. 706-711. ACM, Philadelphia (2006)
9. Falkowski, T., Bartelheimer, J., Spiliopoulou, M. : Mining and Visualizing the Evolution of Subgroups in Social Networks. In: IEEE / WIC / ACM International Conference on Web Intelligence, pp. 52-58. IEEE Computer Society, China (2006)
10. Yang, H., Parthasarathy, S., Mehta, S. : A generalized framework for mining spatio-temporal patterns in scientific data. In: Grossman, R., Bayardo, R. J., Bennett, K. P. (eds.) Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 716-721. ACM, Illinois (2005)
11. Baron, S., Spiliopoulou, M. : Monitoring Change in Mining Results. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) Data Warehousing and Knowledge Discovery, Third International Conference. LNCS, vol. 2114. Springer, Germany (2001)
12. Baron, S., Spiliopoulou, M. : Monitoring the Evolution of Web Usage Patterns. In: Berendt, B., Hotho, A., Mladenic, D., Someren, M., Spiliopoulou, M., Stumme, G. (eds.) Web Mining: From Web to Semantic Web, First European Web Mining Forum. LNCS, vol. 3209. Springer, Croatia (2004)
13. Lu, Y-H., Huang, Y. : Mining data streams using clustering. In: Proceedings of the 4th International Conference on Machine Learning and Cybernetics, pp. 2079-2083. IEEE Computer Society, China (2005)
14. Aggarwal, C. C. : On Change Diagnosis in Evolving Data Streams. IEEE Trans. Knowl. Data Eng. (17), 587-600 (2005)
15. Chen, K., Liu, L. : Detecting the Change of Clustering Structure in Categorical Data Streams. In: Ghosh, J., Lambert, D., Skillicorn, D. B., Srivastava, J. (eds.) Proceedings of the 6th SIAM International Conference on Data Mining. SIAM, USA (2006)
16. Aggarwal, C. C., Han, J., Wang, J., Yu, P. S. : A Framework for Change Diagnosis of Data Streams. In: Halevy, A. Y., Ives, Z. G., Doan, A. : Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 575-586. ACM, California (2003)
17. O'Callaghan, L., Meyerson, A., Motwani, R., Mishra, N., Guha, S. : Streaming-Data Algorithms for High-Quality Clustering. In: Proceedings of the 18th International Conference on Data Engineering. IEEE Computer Society, California (2002)
18. Elnekave, S., Last, M., Maimon, O. : Incremental Clustering of Mobile Objects. ICDE Workshops (2007)
19. Kalnis, P., Mamoulis, N., Bakiras, S. : On Discovering Moving Clusters in Spatio-temporal Data. In: Medeiros, C. B., Egenhofer, M. J., Bertino, E. (eds.) Advances in Spatial and Temporal Databases, 9th International Symposium. LNCS, vol. 3633, pp. 364-381. Springer, Brazil (2005)
20. Li, T., Ma, S., Ogihara, M. : Entropy-based criterion in categorical clustering. In: Proceedings of the 21th international conference on Machine learning. ACM, New York (2004)
21. Zhang, T., Ramakrishnan, R., Livny, M. : BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 103-114. ACM Press, Canada(1996)
22. Kaur, S., Bhatnagar, V., Mehta, S., Kapoor, S.: Concept Drift in Unlabeled Data Stream. Technical Report, University of Delhi (2009)
23. Rousseeuw, P. J. : Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 53-65 (1987)
24. Spinosa, E. J., Ponce de Leon Ferreira de Carvalho, A., Gama, J.: OLINDDA: a clusterbased approach for detecting novelty and concept drift in data streams. In: Proceedings of the 22nd Annual ACM Symposium of Applied Computing. SAC, vol. 1, pp. 318-332. ACM, Seoul, South Korea (2007)
25. Fanizzi, N., d'Amato, C., Esposito, F.: Conceptual clustering and Its Application to Concept Drift and Novelty Detection. In: Proceedings of the 5th European semantic web conference on the semantic web (ESWC). LNCS, vol. 5021, pp. 448-452. Springer-Verlag, Tenerife, Spain (2008)