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Classification Data Mining for Digital Home Sensor Networks

  • Lindong Liu
  • Ruqi Zhou
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 124)

Abstract

The state-of-the-art of digital home sensor network is analyzed and studied. A classification mining model for digital home sensor network is proposed. The data collected by the sensor network is preprocessed and mined with classification by utilizing the FP-tree algorithm.Based on this, the temperature, humidity and noise data with respect to a certain appliance are mined. An improved Apriori algorithm is applied to mine them with classification and to obtain the frequent item sets, the frequent patterns and the classification rules.The results can support the safe running and energy-efficient control of household appliances.

Keywords

Sensor Network Association Rule Frequent Itemsets Household Appliance Apriori Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lindong Liu
    • 1
    • 2
  • Ruqi Zhou
    • 1
  1. 1.Department of Computer ScienceGuangdong University of EducationGuangzhouChina
  2. 2.School of Computer Science&EngineeringSouth China University of TechnologyGuangzhouChina

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