Abstract
Recently, the analysis of power load in the electrical industry has becomes an important element for the concern of customer safety. In power system related studies, data mining techniques are used in power load analysis and they can help decision making in the electrical industry. In this paper, for using emerging patterns to define and analyze the significant difference of safe and non-safe power load lines, and identifying which line is potentially unsafe, we proposed an incremental TFP-tree algorithm for mining emerging patterns that can search efficiently within memory limitation. Especially, the use of two different minimum supports makes the algorithm possible to mine most number of emerging patterns and efficiently handle the incrementally increased, large size of data sets such as power consumption data.
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References
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. ACM Special Interest Group on Management of Data, 1–12 (2000)
Agrawal, M., Jailia, M.: An Interactive Method for Generalized Association Rule mining using FP-tree. In: 2nd Bangalore Annual Compute Conference, vol. (10) (2009)
Agrawal, R., Srikant, R.: Fast algorithm for mining Association rules. In: International Conference on Very Large Data Bases, pp. 487–499 (1994)
Li, X., Deng, Z., Tang, S.: A Fast Algorithm for Maintenance of Association Rules in Incremental Databases. Advanced Data Mining and Applications, 56–63 (2006)
Cheung, W.: Frequent Patterns mining Without Candidate Generation or Support Constraint: Master’s thesis, University of Alberta (2002)
Coenen, F.: The TFP Association Rule Mining Algorithm, Department of Computer Science, The University of Liverpool, UK (2004), http://www.csc.liv.ac.uk/~frans/KDD/Software/Apriori_TFP/aprioriTFP.html
Coenen, F., Leng, P., Ahmed, S.: T-Trees, Vertical Partitioning and Distributed Association Rule Mining. In: International Conference on Data Mining, pp. 513–516 (2003)
Coenen, F., Goulbourne, G., Leng, P.: Computing Association Rules Using Partial Totals. In: Principles and Practice of Knowledge Discovery in Databases, pp. 54–66 (2001)
Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: ACM SIGKDD Int’l Conference Knowledge Discovery and Data Mining, pp. 43–52 (1999)
Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by Aggregating Emerging Patterns. Discovery Science, 737–751 (1999)
Hong, T.P., Wang, C.Y., Tao, Y.H.: A new Incremental Data Mining Algorithm Using Pre-large Itemsets. Intelligent Data Analysis 5(2), 111–129 (2001)
Cheung, W.D., Wong, C.Y., Han, J., Ng, V.T.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: International Conference on Data Engineering, pp. 106–114 (1996)
Fayyad, U., Irani, K.: Multi-Interval discretization of contunuous-valued attributes for classification learning. In: International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1993)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3), 131–163 (1997)
Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines: Technical Report MST-TR-98-14 (1998)
Quinlan, J.: C4.5: programs for Machine Learning. Morgan Stanley, San Mateo (1993)
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Piao, M., Lee, J.B., Shon, H.S., Yun, U., Ryu, K.H. (2011). Enumeration Tree Based Emerging Patterns Mining by Using Two Different Supports. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_86
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DOI: https://doi.org/10.1007/978-3-642-24082-9_86
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