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Hypergraph Clustering Model Based on Fuzzy Frequent Itemsets Applied in Management of Agricultural Land Evaluation

机译:基于模糊频繁项目的经常应用在农业土地评估管理中的超图聚类模型

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There are two shortages in usual methods for agricultural land evaluation: (1) too many manual interferences into the calculating procession, (2) the relatively large differences of partial units are concealed in certain factors. We designed a hyper graph clustering model in this paper based on fuzzy frequent item sets to conduct the evaluation for quality of agricultural land. The database for land units is composed with the fuzzy feature vectors. It executes the mining association rules to the fuzzy item sets given by the definition of evaluation factors, and analyzes the clusters with the HMETIS segmentation method, finally devises the transactions similarity function to cluster the services. With the following checking example in some region in South China, it not only determines the quality rating of each units of agricultural land, but also gives the quality description for each grade.
机译:农业土地评估的平常方法有两种短缺:(1)在计算游行中的手动干扰太多,(2)部分单位的相对较大的差异被隐藏在某些因素中。我们在本文中设计了一种基于模糊频繁项目集的超图形聚类模型,以进行农业土地质量评估。陆地单位数据库由模糊特征向量组成。它将挖掘关联规则执行到由评估因子定义给出的模糊项目集,并通过HMETIS分段方法分析群集,最后向交易相似函数进行集群。在华南地区的下列检查示例中,它不仅确定了农业土地每个单位的质量等级,而且还为每个等级提供了质量描述。

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