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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/101102


    Title: Significant Correlation Pattern Mining in Smart Homes
    Authors: Chen, Yi-Cheng;Peng, Wen-Chih;Huang, Jiun-Long;Lee, Wang-Chien
    Contributors: 淡江大學資訊工程學系
    Keywords: Correlation pattern;smart home;sequential pattern;time interval– based data;usage representation
    Date: 2015-04-01
    Issue Date: 2015-04-11 00:38:42 (UTC+8)
    Publisher: ACM
    Abstract: 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.
    Relation: ACM Transactions on Intelligent Systems and Technology 6(3)Article35, pp.1-23
    DOI: 10.1145/2700484
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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