Browse > Article

TFP tree-based Incremental Emerging Patterns Mining for Analysis of Safe and Non-safe Power Load Lines  

Lee, Jong-Bum (충북대학교 컴퓨터과학과)
Piao, Ming Hao (충북대학교 컴퓨터과학과)
Ryu, Keun-Ho (충북대학교 소프트웨어학과 및 컴정연)
Publication Information
Spatial Information Research / v.19, no.2, 2011 , pp. 71-76 More about this Journal
Abstract
In this paper, for using emerging patterns to define and analyze the significant difference of safe and non-safe power load lines, and identify which line is potentially non-safe, we proposed an incremental TFP-tree algorithm for mining emerging patterns that can search efficiently within limitation of memory. Especially, the concept of pre-infrequent patterns pruning and use of two different minimum supports, made the algorithm possible to mine most emerging patterns and handle the problem of mining from incrementally increased, large size of data sets such as power consumption data.
Keywords
Emerging Patterns; incremental mining; TFP-tree;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. Han, J. Pei, and Y. Yin, 2000, "Mining Frequent Patterns without Candidate Generation," ACM Special Interest Group on Management of Data, pp. 1-12.
2 G. Dong, X. Zhang, L. Wong, and J. Li, 1999, "CAEP: Classification by Aggregating Emerging Patterns," Discovery Science, pp. 737-751.
3 J. C. Platt, 1998, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," Technical Report MST-TR-98-14, Microsoft Research.
4 박지웅, 홍동숙, 김동오, 한기준, 2006, "시공간 이동 패턴 추출을 위한 효율적인 알고리즘," 한국공간정보시스템학회 논문지, 제8권 제2호, pp. 39-52.
5 X. Li, Z. Deng, and S. Tang, 2006, "A Fast Algorithm for Maintenance of Association Rules in Incremental Databases," Advanced Data Mining and Applications, pp. 56-63.
6 D. W. Cheung, C. Y. Wong, J. Han, and V. T. Ng, 1996, "Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique," International Conference on Data Engineering, pp. 106-114.
7 F. Coenen, 2004, "The TFP Association Rule Mining Algorithm," Department of Computer Science, The University of Liverpool, UK, http://www.csc.liv.ac.uk/-frans/KDD/Software/ Apriori_TFP/aprioriTFP.html.
8 박상승, 손호선, 이동규, 지은미, 김희석, 류근호, 2009, "u-City응용에서의 시간 패턴을 이용한 단기 전력 부하 예측," 한국공간정보시스템학회 논문지, 제11권 제2호, pp. 177-181.
9 W. Li, J. Han, and J. Pei, 2001,"CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rule," International Conference on Data Mining, pp. 369-376.
10 G. Dong, and J. Li, 1999, "Efficient Mining of Emerging Patterns: Discovering Trends and Differences," ACM SIGKDD Int'l Conference Knowledge Discovery and Data Mining, pp. 43-52.
11 U. Fayyad, and K. Irani, 1993, "Multi-Interval discretization of countinuous-valued attributes for classification learning," international joint conference on artificial intelligence, pp. 1022-1027.
12 N. Friedman, D. Geiger, and M. Goldszmidt, 1997, "Bayesian network classifiers," Machine Learning, Vol.29, No.2-3, pp. 131-163.   DOI
13 W. C. Hong, 2009, "Electric load forecasting by support vector model," Applied Mathematical Modeling, Vol. 33, pp. 2444-2454.   DOI   ScienceOn
14 T. P. Hong, C. Y. Wang, and Y. H. Tao, 2001, "A new Incremental Data Mining Algorithm Using Pre-large Itemsets," Intelligent Data Analysis, Vol. 5, No. 2, pp. 111-129.
15 Q. Dou, K. Fu, H. Zhu, P. Jiang, and Z. Shi, 2010, "Associated Clustering and Classification Method for Electric Power Load Forecasting," IFIP Advances in Information and Commu-nication Technology, Vol. 340/2010, pp. 112-121.
16 R. Agrawal, and R. Srikant, 1994, "Fast algorithm for mining Association rules," International Conference on Very Large Data Bases, pp. 487-499.
17 J. R. Quinlan, 1993, "C4.5: programs for Machine Learning," Morgan-Kaufmann publishers, San Mateo, CA.