Land evaluation based on agglomerative hierarchical cluster algorithm combining with supervised learning algorithm
Qiang Chen · Computer Engineering and Applications Journal · 2007
By reason of the amount of land evaluation labeled by the experts is limited,a land evaluation method of combining supervised and unsupervised learning algorithm is proposed in this paper.Extracting land evaluation association rules by training a small amount of labeled samples as the supervised information,combining with the chameleon algorithm as the unsupervised method,the land evaluation method utilizes relative interconnectivity and comparability as the measurement to cluster the unlabeled samples,which takes full advantage of high accuracy of supervised learning classification and no necessity demarcated study samples.Experimental results of Guangdong province land resource demonstrate that,by only using 300 training samples chosen randomly,a 94.418 4% correct area rate of land evaluation can be obtained. It provides a higher precision with the accuracy improved by 4.904 1%,comparing with the results of the method cluster in the same condition.