Title :
Using Significant Classification Rules to Analyze Korean Customers´ Power Consumption Behavior: Incremental Tree Induction using Cascading-and-Sharing Method
Author :
Piao, Minghao ; Li, Meijing ; Ryu, Keun Ho
Author_Institution :
Database/Bio Inf. Lab., Chungbuk Nat. Univ., Cheongju, South Korea
fDate :
June 29 2010-July 1 2010
Abstract :
Power load analysis is an important issue in electrical industry. Data mining techniques are widely studied methodology for power load analysis and it helps decision making on electrical industry. In this paper, we propose an incremental tree induction algorithm using Cascading-and-Sharing method, and use mined significant classification rules to analyze customers´ power consumption behavior in General, Education and Regular groups.
Keywords :
data mining; decision making; decision trees; electricity supply industry; load (electric); pattern classification; power consumption; power engineering computing; Korean customers power consumption behavior analysis; cascading-and-sharing method; data mining technique; decision making; electrical industry; incremental tree induction algorithm; power load analysis; significant classification rules; Classification algorithms; Classification tree analysis; Data mining; Load forecasting; Temperature sensors; Training; decision tree induction; incremental mining; power consumption behavior; significant classification rules;
Conference_Titel :
Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on
Conference_Location :
Bradford
Print_ISBN :
978-1-4244-7547-6
DOI :
10.1109/CIT.2010.503
