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An application of improved fuzzy C means clustering algorithm in tax administration

机译:改进模糊C算法在税务管理中的应用

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Tax sources category management is an important part in the tax administration. An important part of tax sources category is efficiency and rationality. An improved fuzzy clustering method used in tax sources classification is presented in this paper. This algorithm solves the disadvantage of losing information generated by hard classification of traditional clustering methods. The intrinsic characteristics between individuals can be revealed from a large number of tax-related data. The problem of focused management, clear management objectives and optimize resource allocation can be well resolved after taxpayers classified into different clusters. The experimental result also shows that the new improved fuzzy C means clustering algorithm combining with Parzen window estimation can resolve the initial central issue in original algorithm and reduce the clustering iterations.
机译:税收来源类别管理是税收管理的重要组成部分。 税收来源类别的重要部分是效率和理性。 本文介绍了税收源分类中使用的改进的模糊聚类方法。 该算法解决了丢失由传统聚类方法的硬分类产生的信息的缺点。 个人之间的内在特征可以从大量与税收相关数据中透露。 在分类为不同群集之后,专注管理的问题,明确的管理目标和优化资源分配可以很好地解决。 实验结果还表明,新的改进的模糊C算法与Parzen窗口估计相结合可以解决原始算法中的初始中央问题,并减少群集迭代。

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