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yifraternity's Projects

hanlp icon hanlp

自然语言处理 中文分词 词性标注 命名实体识别 依存句法分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁

kms icon kms

office 2016的激活工具

models icon models

Models and examples built with TensorFlow

numpy icon numpy

The fundamental package for scientific computing with Python.

os-k-means icon os-k-means

现有聚类算法面向高维稀疏数据多未考虑类簇可重叠和离群点的存在,导致聚类效果不理想。针对此,提出一种可重叠子空间K-Means聚类算法(An Overlapping Subspace K-Means Clustering Algorithm, OS-K-Means)。给出类簇子空间计算策略,在聚类过程中动态更新每个类簇的属性子空间,并定义合理的约束函数指导聚类过程,从而实现类簇的可重叠性与寻找离群点的效果。具体地,定义合理的目标函数对传统的K-Means算法进行修正,利用熵权约束分别计算每个类簇中每个维度的权重,使用权重值来标识对不同类簇中维度的相对重要性,并加入对重叠程度和离群值数量控制的参数。

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