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hyperspherical相关的网络例句

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与 hyperspherical 相关的网络例句 [注:此内容来源于网络,仅供参考]

To analyze and compare the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, conclude that Fuzzy c-Means clustering does not work well if the underlying classes or clusters deviate strongly from hyperspherical structures, aiNet is capable of reducing redundancy, describing data structure, including the shape of clusters by experiments on relatively gathering data set, scattering data set, annular data set and corkscrew data set.

分析比较了模糊C 均值聚类算法FCM 和人工免疫网络aiNet 聚类算法的特性,通过它们对相对集中数据、稀疏分散数据、环状和螺旋分布数据聚类来进行比较,得到当遇到潜在的类或簇背离超球面结构时,FCM 算法表现不佳,而aiNet 可以很好的发现数据的内在特征,减少数据中的冗余、描述数据结构和聚类形状,表现良好的适应性。

This algorithm could find optimal initial starting points applying iterative max-min distance means and then combined the small clusters from given data set into final ones,for an elongated or large cluster could be considered as the union of a few small distinct hyperspherical clusters.

运用两阶段最大最小距离法搜索出最佳初始聚类中心,将原始数据集分割成小类后用合并算法形成最终类,即用多个聚类中心联合代表一个延伸状或者较大形状的簇。

This method uses what Professor Salton has proposed as the vector spacial model to express text as an invisible vector in hyperspherical space.It makes use of certain pre-disposal methods (reduce the dimensions,clustering thesaurus,filtering out old-fashioned words) to reduce the dimensions.It usescosines to measure the degree of similaritybetween texts and uses the total similarity as the target convergence vector.It produces clustered results by using restricted iteratives to achieve partial perfection.

该算法用Salton教授提出的向量空间模型把文本表示成不可视的超球向量空间的一个向量,采用一定的预处理手段(降维、同义词归并、停用词过滤)完成维数约减,使用余弦相似度度量文本之间的相似度,并选择总凝聚度作为目标收敛函数,通过有限次的迭代达到局部最优,输出聚类结果。

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