Mining correlation patterns among appliances in smart home environment

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

Research output: Contribution to journalConference articlepeer-review

22 Scopus citations


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.

Original languageEnglish
Pages (from-to)222-233
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8444 LNAI
Issue numberPART 2
StatePublished - 1 Jan 2014
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan
Duration: 13 May 201416 May 2014


  • correlation pattern
  • sequential pattern
  • smart home
  • time interval-based data
  • usage representation

Fingerprint Dive into the research topics of 'Mining correlation patterns among appliances in smart home environment'. Together they form a unique fingerprint.

Cite this