Classification Data Mining for Digital Home Sensor Networks

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.

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