Significant correlation pattern mining in smart homes

Y. I.Cheng Chen, Wen Chih Peng, Jiun Long Huang, Wang-chien Lee

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number35
JournalACM Transactions on Intelligent Systems and Technology
Volume6
Issue number3
DOIs
StatePublished - Apr 1 2015

Fingerprint

Smart Home
Mining
Miners
Pattern Discovery
Sensors
Pruning
Search Space
Statistical Model
Filter
Sensor

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

Cite this

Chen, Y. I.Cheng ; Peng, Wen Chih ; Huang, Jiun Long ; Lee, Wang-chien. / Significant correlation pattern mining in smart homes. In: ACM Transactions on Intelligent Systems and Technology. 2015 ; Vol. 6, No. 3.
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Significant correlation pattern mining in smart homes. / Chen, Y. I.Cheng; Peng, Wen Chih; Huang, Jiun Long; Lee, Wang-chien.

In: ACM Transactions on Intelligent Systems and Technology, Vol. 6, No. 3, 35, 01.04.2015.

Research output: Contribution to journalArticle

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