淡江大學機構典藏:Item 987654321/98057
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62805/95882 (66%)
造訪人次 : 3994269      線上人數 : 287
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    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


    題名: Mining Correlation Patterns among Appliances in Smart Home Environment
    作者: Chen, Yi-Cheng;Chen, Chien-Chih;Peng, Wen-Chih;Lee, Wang-Chien
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: correlation pattern;smart home;sequential pattern;time intervalbased data;usage representation
    日期: 2014-05
    上傳時間: 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
    顯示於類別:[資訊工程學系暨研究所] 會議論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    PAKDD'14-Mining Correlation Patterns among Appliances in Smart Home Environment.pdf2498KbAdobe PDF1569檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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