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RESEARCH AND APPLICATION ON KNN METHOD BASED ON CLUSTER BEFORE CLASSIFICATION

机译:基于分类前基于簇的KNN方法研究与应用

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In order to mine the hidden knowledge, and solve the problem of "data is superfluous but knowledge is spare", data mining is used widely. Data classification is an important task of data mining, which has been at the center of research interest in recent years. The research of this paper is on classification. Starting from fuzzy KNN classification method, based on the idea of cluster before classification, a method-KNN method based on cluster before classification - is proposed. Paper executes particular and extensive experiments, which include two parts: validating the influence the parameters have on new method and comparing the expansibility of the new classification method and fuzzy KNN method with the increasing of the dataset size and the number of attributes. The experimental results show the benefits of new method when dealing with larger datasets. Finally, the proposed method is applied to data preprocessing. Executing the classification on vector map datasets about vegetation, the vegetation style can be obtained, so data preprocessing is completed; we also display the classification results using the software of geography information system such as ArcGIS.
机译:为了挖掘隐藏的知识,解决“数据是多余的,但知识备用”的问题,数据挖掘广泛使用。数据分类是数据挖掘的重要任务,近年来一直是研究兴趣的中心。本文的研究是在分类上。从模糊KNN分类方法开始,基于分类前的集群思想,提出了一种基于分类之前的簇的方法-KNN方法。纸张执行特定和广泛的实验,其中包括两部分:验证对新方法的影响以及对数据集大小的增加和属性数量的新分类方法和模糊KNN方法的扩展性。实验结果表明,在处理较大数据集时的新方法的好处。最后,将所提出的方法应用于数据预处理。在植被的矢量地图数据集上执行分类,可以获得植被风格,因此数据预处理完成;我们还使用诸如ArcGIS的地理信息系统软件显示分类结果。

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