Share This Article:

A Novel Data Preparation Approach for Target Based Association Rule Mining

Abstract Full-Text HTML XML Download Download as PDF (Size:3521KB) PP. 249-255
DOI: 10.4236/wsn.2014.611024    5,159 Downloads   5,693 Views  

ABSTRACT

Target based association rules extract the correlation between targets in and around strategically specified regions. These rules can be used to predict and estimate the future results and find the faulty targets, etc. To extract these rules efficiently and effectively data preparation is required. The existing mechanisms of data preparation have the problem of redundancy in the data. As a consequence, extra energy is required for the sensors which used to monitor the targets. In this paper, authors propose a novel data preparation approach for target based association rule mining from the point of coverage wireless sensor networks, which reduces the redundancy in the data and thus enhances the performance of the network.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Singh, P. and Gupta, R. (2014) A Novel Data Preparation Approach for Target Based Association Rule Mining. Wireless Sensor Network, 6, 249-255. doi: 10.4236/wsn.2014.611024.

References

[1] Stankovic, S.V., Rakocevic, G., Kojic, N. and Mllicev, D. (2012) A Classification and Comparison of Data Mining Algorithms for Wireless Sensor Networks. 2012 IEEE International Conference on Industrial Technology, Athens, 19-21 March 2012, 265-270.
[2] Halatchev, M. and Gruenwald, L. (2005) Estimating Missing Values in Related Sensor Data Streams. Advances in Data Management.
[3] Romer, K. (2006) Distributed Mining of Spatio-Temporal Event Patterns in Sensor Networks, NCCR-MICS.
[4] Loo, K.K., Tong, I., Kao, B. and Cheung, D. (2005) Online Algorithms for Mining Inter-Stream Association from Large Sensor Networks. HKU CS Tech Report.
[5] Agrawal, R., Imielinski, T. and Swami, A.N. (1993) Mining Association Rules between Sets of Items in Large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 22, 207-216.
[6] Boukerchem, A. and Samarah, S. (2007) A New Representation Structure for Mining Association Rules from Wireless Sensor Networks. University of Ottawa, Ottawa.
[7] Boukerche, A. and Samara, S. (2008) A Novel Algorithm for Mining Association Rules in Wireless ad Hoc Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 19, 865-877.
http://dx.doi.org/10.1109/TPDS.2007.70789
[8] Samarah, S., Habyalimana, A.S. and Boukerche, A. (2009) Target-Based Association Rules for Point-of-Coverage Wireless Sensor Networks. IEEE Symposium on Computers and Communications, Sousse, 5-8 July 2009, 938-943.
[9] Samarah, S. and Boukerche, A. (2011) Target Association Rules: A New Behavioral Patterns for Point of Coverage Wireless Sensor Networks. IEEE Transactions on Computers, 60, 879-889.
http://dx.doi.org/10.1109/TC.2010.227
[10] Heinzelman, W.R., Chandrakasan, A. and Balakrishnan, H. (2000) Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Proceedings of the 33rd Hawaii International Conference on System Sciences, 4-7 January 2000, 8020-8030.
[11] Mathur, G., Desnoyers, P., Ganesan, D. and Shenoy, P. (2006) Ultra-Low Power Data Storage for Sensor Networks. Proceeding of the fifth IEEE/ACM Conference on Information Processing in Sensor Networks, Nashville, 19-21 April 2006, 374-381.
[12] Toshiba 128-MBIT (16M8BITS/8Mx16BITS) CMOS NAND E2PROM.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.