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    题名: Significant Correlation Pattern Mining in Smart Homes
    作者: Chen, Yi-Cheng;Peng, Wen-Chih;Huang, Jiun-Long;Lee, Wang-Chien
    贡献者: 淡江大學資訊工程學系
    关键词: Correlation pattern;smart home;sequential pattern;time interval– based data;usage representation
    日期: 2015-04-01
    上传时间: 2015-04-11 00:38:42 (UTC+8)
    出版者: ACM
    摘要: Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
    關聯: ACM Transactions on Intelligent Systems and Technology 6(3)Article35, pp.1-23
    DOI: 10.1145/2700484
    显示于类别:[資訊工程學系暨研究所] 期刊論文

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