Electricity market trade based on mobile
intelligent device will extend the volume of transaction. For the massive and
various trading data, transaction mining algorithm is very useful to find the
relationship of correlative elements such as trade price and power capacity,
and it always occurs between the power users and power generation enterprises.
The novel FP-Table algorithm is proposed in this paper to solve the massive
transaction mining problem. The FP-Table algorithm integrates the Hash table
into FP-Growth algorithm, using two-dimension table saving frequency count of
item pair, then mining the frequency items of electricity transactions
efficiently. Application of mobile transaction mining is proved to be high
efficiency and high value by performance experiment results.
Cite this paper
Gao, C. , Dai, Y. and Jiao, M. (2015) Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market. Open Journal of Social Sciences
, 79-84. doi: 10.4236/jss.2015.37014
 Deb, R.K., Hsue, L.L., Albert, R. and Christian J.E. (2001) Multi-Market Modeling of Regional Transmission Organization Functions. The Electricity Journal, 14, 39-54. http://dx.doi.org/10.1016/S1040-6190(01)00174-9
 Agrawal, R., Imieliński, T. and Swami, A. (1993) Mining Association Rules between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, New York, June 1993, 207-216. http://dx.doi.org/10.1145/170035.170072
 Smyth, P., Pregibon, D. and Faloutsos, P. (2002) Data-Driven Evolution of Data Mining Algorithms. Communications of the ACM, 45, 33-37. http://dx.doi.org/10.1145/545151.545175
 Smyth, R. (2000) Theory of Dependence Values. ACM Transactions on Database Systems, 25, 380-406.
 Lakshmanan, L.V.S., Ng, R., Han, P. and Pang P. (1999) Optimization of Constrained Frequent Set Queries with 2-Variable Constraints. Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia, May 1999, 157-168. http://dx.doi.org/10.1145/304182.304196
 Lu, H., Feng, L. and Han, J. (2000) Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules. ACM Transactions on Information Systems, 18, 423-454.
 Mabroukeh, N.R. and Ezeife, C.I. (2010) A Taxonomy of Sequential Pattern Mining Algorithms. ACM Computing Surveys, 43, 1-41. http://dx.doi.org/10.1145/1824795.1824798
 Lee, C.H., Lin, C.R. and Chen, M.S. (2001) Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining. Proceedings of the Tenth international conference on Information and Knowledge Management, Atlanta, October 2001, 263-270. http://dx.doi.org/10.1145/502585.502630
 Patil, S.P. and Patewar, T.M. (2012) A Novel Approach for Efficient Mining and Hiding of Sensitive Association Rule. Proceedings of the 2012 Nirma University International Conference on Engineering, Ahmedabad, 6-8 December 2012, 1-6. http://dx.doi.org/10.1109/nuicone.2012.6493184
 Singh, A. and Agarwal, J. (2014) Proposed Algorithm for Frequent Item Set Generation. Proceedings of the 2014 Seventh International Conference on Contemporary Computing, Noida, 7-9 August 2014, 160-165.
 Prasanna, K. and Seetha, M. (2012) Mining High Dimensional Association Rules by Generating Large Frequent K- Dimension Set. Proceedings of the 2012 International Conference on Data Science & Engineering, Cochin, 18-20 July, 2012, 58-63. http://dx.doi.org/10.1109/ICDSE.2012.6282304
 Ariya, A. and Kreesuradej, W. (2013) Probability-Based Incremental Association Rule Discovery Using the Normal Approximation. Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration, San Francisco, 14-16 August 2013, 432-439.