该文提出了一种粗糙谱聚类算法，并将其应用于文本数据挖掘。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
然后分析了各种传统聚类算法在入侵检测中所表现的不足，并引入了谱聚类算法加以解决。
Then introduces spectral clustering algorithm based on deficiencies of variety of traditional clustering algorithms on intrusion detection.
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.
从多方面分析了该算法的性能，并将该算法应用于酵母细胞周期的芯片表达谱数据聚类。
The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data.
在大规模基因表达谱的数据分析中引入了一种全新的基于贝叶斯模型的聚类算法。
A novel clustering algorithm based on Bayesian model was introduced into the analysis of large-scale gene expression profiles.
为了对低信噪比的超声图像进行有效分割，提出一种谱聚类集成的超声图像分割算法。
A novel ultrasound image segmentation algorithm, which is based on the spectral cluster ensemble, is proposed to segment ultrasound images with low SNR.
基于肿瘤基因表达谱研究了肿瘤相关基因及其功能模块的聚类算法，同时利用模块度评价了算法的有效性。
Second, the modularity function which was used to assess the performance of clustering algorithm was applied to the colon cancer gene modules.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
本文提出的两个算法能够在有限的计算资源(例如：CPU和内存)的条件下最大化谱聚类的聚类精确度。
Our two improved spectral clustering algorithms use the limited computing resources (e. g. : memory, CPU) to maximize the accuracy of spectral clustering.
主要工作和成果如下：①对谱聚类基本原理和典型算法做了较为全面的分析和研究，利用谱聚类的特性实现了在复杂数据集上的聚类。
We focus on finding abnormity in datasets with clustering and classified structure and studying the implement and optimization of key technology for outlier detection in this paper.
主要工作和成果如下：①对谱聚类基本原理和典型算法做了较为全面的分析和研究，利用谱聚类的特性实现了在复杂数据集上的聚类。
We focus on finding abnormity in datasets with clustering and classified structure and studying the implement and optimization of key technology for outlier detection in this paper.
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