文本聚类算法的设计与实现

为了有效地提高丈本聚类的质量和效率,在对已有的层次聚类和K-means算法分析和研究的基础上,针对互联网信息处理量大、实时性高的特点,设计并实现了一种用于高维稀疏相似矩阵的文本聚类算法.该算法结合了层次聚类和K-means聚类的思想,根据一个阈值来控制聚类算法的选取和新簇的建立,并通过文本特征提取和文档相似度矩阵计算实现文本聚类.实验结果表明,该算法的召回率和正确率更高.

Research and implementation of text clustering algorithm

To improve the quality and efficiency of text clustering effectively, based on the analysis and research of the hierarchical clustering and k-means algorithms, a kind of text clustering algorithm for a higher-dimensional sparse matrix is designed and implemented for the characteristic of large quantity of internet information and high real-time. The algorithm combines the ideas of the hierarchical clustering and K-means clustering, which controls the selection of clustering algorithm and the establishment o...