本文提出一种以模式聚类为基础的病态样本判定方法，并给出基于模式相似度计算的投票剔除算法。
The author presented a method for morbid sample recognition that base mode clustering, paper proposed a eliminating algorithm of voting that base mode similarity calculating.
基于形式概念分析和概念相似度，给出一种新的多背景文本模糊聚类方法和模型。
A novel multi-context text fuzzy clustering method and its model based on formal concept analysis and concept similarity is proposed.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
基于包含度理论，提出不精确数据的一种相似度，并讨论基于此相似度的不精确数据的聚类方法。
In this paper, a kind of similarity degree for non precise data is proposed and based on this similarity degree, some clustering methods for non precise data are investigated.
再采用基于互联合矩阵的集成方法，在构造的相似度矩阵上应用谱聚类算法来完成对数据的最后聚类。
Then using the combining method of co-association matrix, the final result is obtained by using spectral clustering algorithm on this matrix.
基于形式概念分析和概念相似度，给出了一种新的文本模糊聚类方法。
A novel text fuzzy clustering based on formal concept analysis and concept similarity are proposed.
针对校园论坛中的文档数据进行聚类，该方法降低了处理的复杂度同时提高了相似度计算的准确性。
Do the experiment on the documents data of campus forum, this method reduces the computer processing complexity and improves the veracity of similarity calculation.
再按照层次聚类的方法，合并连接相似度高的子簇，得到最终的聚类结果。
Then it combines the sub-clusters with higher similarities in the hierarchical clustering method and gets the final clustering result.
因此考虑一种次优可行的基于聚类颜色特征的相似度计算方法。
So consider a second-best possible color feature based on clustering the similarity calculation.
根据抽样指标的观测值，选择某种度量样品之间的指标相似度的度量方法，并以此为聚类依据；
According to the observations of a sample of indicators, a measure of samples to choose between the targets of the similarity measurement method and use it as the basis for clustering;
根据抽样指标的观测值，选择某种度量样品之间的指标相似度的度量方法，并以此为聚类依据；
According to the observations of a sample of indicators, a measure of samples to choose between the targets of the similarity measurement method and use it as the basis for clustering;
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