A Novel Approach of Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - April Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-4 | ||
| Year of Publication : 2013 | ||
| Authors : Alla Naga Sravya,M.Nalini Sri |
Alla Naga Sravya,M.Nalini Sri"A Novel Approach of Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations"International Journal of Computer Trends and Technology (IJCTT),V4(4):624-629 April Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing information over temporal data. In this paper, we present a temporal data clustering framework via a weighted clustering ensemble of multiple partitions produced by initial clustering analysis on different temporal data representations. In our approach, we propose a novel weighted consensus function guided by clustering validation criteria to reconcile initial partitions to candidate consensus partitions from different perspectives, and then, introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a result, the proposed weighted clustering ensemble algorithm provides an effective enabling technique for the joint use of different representations, which cuts the information loss in a single representation and exploits various information sources underlying temporal data. In addition, our approach tends to capture the intrinsic structure of a data set, e.g., the number of clusters. Our approach has been evaluated with benchmark time series, motion trajectory, and time-series data stream clustering tasks. Simulation results demonstrate that our approach yields favourite results for a variety of temporal data clustering tasks. As our weighted cluster ensemble algorithm can combine any input partitions to generate a clustering ensemble, we also investigate its limitation by formal analysis and empirical studies.
References-
[1] Bagnall, A., Ratanamahatana, C. A., et al. (2009). "A bit level representation for temporal data mining with shape based similarity." Data Mining and Knowledge Discovery 13(1): 11-40.
[2] Cheng, H., Hua, K. A., et al. (2008). "Constrained locally weighted clustering." Proceedings of the VLDB Endowment 1(1): 90-101.
[3] Ghaemi, R., Sulaiman, M. N., et al. (2009). A Survey: Clustering Ensembles Techniques. World Academy of Science, Engineering and Technology.
[4] Yang, Y. and Chen, K. (2010). supervised Learning via Iteratively Constructed Clustering Ensemble. Proceedings of International Joint Conference on Neural Networks Barcelona, Spain.
[5] Azimi, J., Abdoos, M., et al. (2007). A new efficient approach in clustering ensembles.
Keywords — Temporal data clustering, clustering ensemble, different representations, weighted consensus function, model.