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.
KeywordsSensor Network Association Rule Frequent Itemsets Household Appliance Apriori Algorithm
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- 1.Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., et al.: A survey on sensor networks. IEEE Commun. Maga., 102–114 (2002)Google Scholar
- 3.Gomez, C., Paradells, J.: Wireless home automation networks:A survey of architectures and technologies. IEEE Commun. Maga., 92–101 (2010)Google Scholar
- 5.Yanthy, W., Sekiya, T., Yamaguchi, K.: Mining interesting rules by association and classification algorithms. In: Proc. Forth Int. Conf. Frontier of Computer Sci. Tech., pp. 177–182 (December 2009)Google Scholar
- 6.Ye, Y., Chiang, C.: A parallel Apriori algorithm for frequent itemset mining. In: Proc. Forth Int. Conf. Software Engineering Research, Management and Applications, pp. 7–94 (August 2006)Google Scholar
- 7.Wang, C.: An improved Apriori algorithm for mining association rules. Computer Engineering and Applications, 183–185 (2004)Google Scholar