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 of new clusters through a threshold and realizes text clustering through extraction of text feature and calculation of text similarity matrix. Experiments showed that the accuracy and recall rate of this algorithm are higher.