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《Journal of Anhui Agricultural Sciences》 2009-30
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The Warning Model of Melon Thrips Based on k-mean Clustering Combining Fuzzy Association Rules Algorithm

CHEN Zhi-min et al (Center of Experimental Teaching for Common Basic Courses,South China Agricultural University,Guangzhou,Guangdong 510642)  
[Objective] The aim of the study was to improve warning effect of melon thrips diseases and insect pests.[Method]The warning model of melon thrips by k-mean Clustering was firstly established.Considering the challenge of quantity of samples that were difficult to classify in the process of k-mean Clustering,an iterative algorithm combining fuzzy association rules was discussed.[Result] The study showed that the warning accuracy of k-mean Clustering combining fuzzy association rules provided a higher accuracy rate than that of nearest Neighbor Clustering,k-means Clustering and Support Vector Machine in the same condition.[Conclusion] k-mean Clustering combining fuzzy association rules algorithm could effectively improve warning accuracy rate.
【Fund】: 华南农业大学校长基金项目(2007K017)
【CateGory Index】: S433
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