Mining correlation patterns among appliances in smart home environment

Yi Cheng Chen, Chien Chih Chen, Wen-Chih Peng, Wang Chien Lee

研究成果: Conference article同行評審

22 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)222-233
頁數12
期刊Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8444 LNAI
發行號PART 2
DOIs
出版狀態Published - 1 一月 2014
事件18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan
持續時間: 13 五月 201416 五月 2014

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