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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Mining Correlation Patterns among Appliances in Smart Home Environment
verfasst von : Yi-Cheng Chen, Chien-Chih Chen, Wen-Chih Peng, Wang-Chien Lee
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer International Publishing
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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.