| Significant Correlation Pattern Mining in Smart Homes | |
|---|---|
| 學年 | 103 |
| 學期 | 2 |
| 出版（發表）日期 | 2015-04-01 |
| 作品名稱 | Significant Correlation Pattern Mining in Smart Homes |
| 作品名稱（其他語言） | |
| 著者 | Chen, Yi-Cheng; Peng, Wen-Chih; Huang, Jiun-Long; Lee, Wang-Chien |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | New York: Association for Computing Machinery, Inc. |
| 著錄名稱、卷期、頁數 | ACM Transactions on Intelligent Systems and Technology 6(3)Article35, pp.1-23 |
| 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. |
| 關鍵字 | Correlation pattern;smart home;sequential pattern;time interval– based data;usage representation |
| 語言 | en_US |
| ISSN | 2157-6912 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | Chen, Yi-Cheng |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/101102 ) |
GoogleAnalytics