淡江大學機構典藏:Item 987654321/98057
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 63246/95943 (66%)
造访人次 : 4850524      在线人数 : 363
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98057


    题名: Mining Correlation Patterns among Appliances in Smart Home Environment
    作者: Chen, Yi-Cheng;Chen, Chien-Chih;Peng, Wen-Chih;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 intervalbased data;usage representation
    日期: 2014-05
    上传时间: 2014-05-22 21:55:43 (UTC+8)
    出版者: Springer
    摘要: Since 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 paper, a novel algorithm, namely, Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. With several new optimization techniques, CoPMiner can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
    關聯: The 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014), pp.222-233
    显示于类别:[資訊工程學系暨研究所] 會議論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    PAKDD'14-Mining Correlation Patterns among Appliances in Smart Home Environment.pdf2498KbAdobe PDF1606检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈