Ascertainable clustering number and large training sets are vital problems of spectral clustering.
自动确定聚类数和海量数据的处理是谱聚类的关键问题。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
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 presents a novel spectral clustering approach to motion segmentation based on motion trajectory.
本文提出了一种新的谱聚类方法基于运动轨迹的运动分割。
Then introduces spectral clustering algorithm based on deficiencies of variety of traditional clustering algorithms on intrusion detection.
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 using the combining method of co-association matrix, the final result is obtained by using spectral clustering algorithm on this matrix.
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。
Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance.
首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。
Our two improved spectral clustering algorithms use the limited computing resources (e. g. : memory, CPU) to maximize the accuracy of spectral clustering.
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。
Main works are as follows. A clustering ensemble algorithm based on prior knowledge and spectral analysis is proposed.
具体工作如下：提出了一种基于先验信息和谱分析的聚类融合算法。
Finally, according to the restriction of the preliminary clustering result derived from spectral feature of objects, the ultimate classification is achieved referring to the rules.
最后，在对象光谱特征的初步分类结果，根据纹理分类规则得到最终结果基础上。
This paper proposes video clips clustering method based on spectral of correlative graph.
提出一种基于关联图谱的视频片段聚类方法。
Graph plotting part consists of histogram, cross section drawing, clustering spectral pattern, 2-dimensinoal factor scattergram.
图形绘制包括：直方图、因子平面散点图、聚类谱系图、剖面图。
Graph plotting part consists of histogram, cross section drawing, clustering spectral pattern, 2-dimensinoal factor scattergram.
图形绘制包括：直方图、因子平面散点图、聚类谱系图、剖面图。
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