A novel system for extracting useful correlation in smart home environment

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

Research output: Contribution to conferencePaper

6 Scopus citations

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 paper, a novel system, namely, Correlation Pattern Mining System (CPMS), is developed to capture the usage patterns and correlations among appliances. With several new optimization techniques, CPMS 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
Pages357-364
Number of pages8
DOIs
StatePublished - 1 Jan 2013
Event2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States
Duration: 7 Dec 201310 Dec 2013

Conference

Conference2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
CountryUnited States
CityDallas, TX
Period7/12/1310/12/13

Keywords

  • Correlation pattern
  • Sequential pattern
  • Smart home
  • Time interval-based data
  • Usage representation

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