Clustering distributed time series in sensor networks

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 678-687

  Yin, Jie ^a   Gaber, Mohamed Medhat ^b  

^a CSIRO   (Australia)

^b MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPACT REPRESENTATION; CRITICAL TASKS; DATA POINTS; DISTRIBUTED TIME; ENERGY EFFICIENT; ENVIRONMENTAL MONITORING; EVENT DETECTION; FALSE ALARM RATE; GLOBAL CLUSTERING; HAAR WAVELET TRANSFORM; HIERARCHICAL STRUCTURES; INCREMENTAL CLUSTERING; NETWORK TOPOLOGY; ON DYNAMICS; SENSOR DATA; SYNTHETIC DATA;

ALARM SYSTEMS; CLUSTER ANALYSIS; ELECTRIC NETWORK TOPOLOGY; ENERGY EFFICIENCY; INFORMATION MANAGEMENT; MINING; SENSOR NODES; TELECOMMUNICATION EQUIPMENT; TIME SERIES; WAVELET TRANSFORMS; WIRELESS TELECOMMUNICATION SYSTEMS;

SENSOR NETWORKS;

EID: 67049160138     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.58     Document Type: Conference Paper

References (24)

1. D. Abadi, S. Madden, and W. Lindner. REED: Robust, efficient filtering and event detection in sensor networks. In Proceedings of the 31st International Conference in Very Large Database (VLDB), pages 769-780, Trondheim, Norway, August-September 2005. (Pubitemid 43991051)
2. J. Beringer and E. Hullermeier. Online clustering of parallel data streams. Data and Knowledge Engineering, 58(2):180-204, 2005.
3. F. K.-P. Chan, A. W.-C. Fu, and C. Yu. Haar wavelets for efficient similarity search of time-series: With and without time warping. IEEE Transactions on Knowledge and Data Engineering, 15(3):686-705, 2003.
4. B.-R. Dai, J.-W. Huang, M.-Y. Yeh, and M.-S. Chen. Clustering on demand for multiple data streams. In Proceedings of the 4th International Conference on Data Mining ICDM, pages 367-370, Brighton, UK, November 2004.
5. B.-R. Dai, J.-W. Huang, M.-Y. yeh, and M.-S. Chen. Adaptive clustering for multiple evolving streams. IEEE Transaction on Knowledge and Data Engineering (TKDE), 18(9):1166-1180, 2006. (Pubitemid 44192185)
6. G. Forman and B. Zhang. Distributed data clustering can be efficient and exact. SIGKDD Explorations, 2(2):115-122, 2000.
7. A. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss. One-pass wavelet decomposition of data streams. IEEE Transactions on Knowledge and Data Engineering, 15(3):541-554, 2003.
8. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006.
9. H. Kargupta, W. Huang, K. Sivakumar, and E. Johnson. Distributed clustering using collective principal component analysis. Knowledge Information Systems, 3:422-448, 2001.
10. L. Kaufman and P. Rousseeuw. Finding Groups in Data: An introduction to Cluster Analysis. Wiler Interscience, 1990.
11. M. Li, Y. Liu, and L. Chen. Non-threshold based event detection for 3d environment monitoring in sensor networks. In Proceedings of the 27th International Conference on Distributed Computing Systems (ICDCS), pages 9-16, Toronto, Ontario, Canada, June 2007.
12. T. Li, Q. Li, S. Zhu, and M. Ogihara. A survey on wavelet applications in data mining. SIGKDD Explorations, 4(2):49-68, 2002.
13. S. Manganaris. Supervised classification with temporal data. PhD thesis, Department of Computer Science, Vanderbilt Universisty, 1997.
14. A. Meka and A. Singh. Distributed spatial clustering in sensor networks. In The International Conference on Extending Database Technology (EDBT), pages 980-1000, Munich, Germany, March 2006. (Pubitemid 43986863)
15. P. Rodrigues, J. Gama, and L. Lopes. Requirements for clustering streaming sensors. In First International Workshop on Knowledge Discovery from Sensor Data, San Jose, CA, USA, August 2007.
16. P. P. Rodrigues, J. Gama, and J. P. Pedroso. ODAC: Hierarchical clustering of time series data streams. In Proceedings of the Sixth SIAM International Conference on Data Mining (SDM), pages 499-503, Bethesda, Maryland, USA, April 2006. (Pubitemid 43955567)
17. P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53-65, 1987. (Pubitemid 18528483)
18. S. Salvador and P. Chan. FastDTW: Toward accurate dynamic time warping in linear time and space. In The KDD Workshop on Mining Temporal and Sequential Data, pages 70-80, Seattle, WA, USA, August 2004.
19. A. C. W. R. Heinzelman and H. Balakrishnan. Energyefficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (HICSS),Maui, HI, USA, January 2000.
20. Y.-L. Wu, D. Agrawal, and A. Abbadi. A comparison of DFT and DWT based similarity search in time-series databases. In The Ninth International Conference on Information and Knowledge Management (CKIM), pages 488- 495, McLean, VA, USA, November 2000.
21. Y.-L. Wu, D. Agrawal, and A. Abbadi. A comparison of DFT and DWT based similarity search in time-series databases. In The Ninth International Conference on Information and Knowledge Management (CKIM), pages 488- 495, McLean, VA, USA, November 2000 . [21] W. Xue, Q. Luo, L. Chen, and Y. Liu. Contour map matching for event detection in sensor networks. In Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 145-156, Chicago, IL, USA, June 2006.
22. F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang. A two-tier data dissemination model for large-scale wireless sensor networks. In Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (Mobi- Com), pages 148-159, Atlanta, GA, USA, September 2002.
23. B.-K. Yi, H. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. In The 24th IEEE International Conference on Data Enginnering (ICDE), pages 201-208, Orlando, FA, USA, February 1998.
24. O. Younis and S. Fahmy. HEED: A hybrid, energy-efficient, distributed clustering approach for ad-hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4):366-379, 2004. (Pubitemid 40010934)