中文摘要：

摘要针对顺序IB（sIB）算法在文本聚类上存在的诸如易陷入局部优解、效率较低等问题，基于模拟退火方法，提出一种优化的顺序文本聚类算法（SA—isIB）．该算法根据一个合理的退火序列，从基本sIB算法产生的初始聚类结果中随机选取一定比例的文本，对其类标记进行随机修改并重新对解进行优化，经过退火过程后，得到比sIB算法精度更高的文本聚类结果．文本数据集上的实验结果表明，SA—isIB能有效提高sIB算法用于文本聚类的精度．

英文摘要：

To solve the problems of local optima and low efficiency in sequential information bottleneck （sIB） algorithm for document clustering, an improved sIB algorithm is proposed, namely SA-isIB. By a reasonable annealing sequence, a certain proportional of documents are selected randomly from the initial clustering solution of basic sIB algorithm. Then the clustering labels of selected documents are revised and the solution is optimized iteratively. After the process of simulated annealing, higher accuracy document clustering solutions are obtained. Experimental results on document datasets show that by using SA-isIB algorithm the accuracy of sIB algorithm for document clustering is improved efficiently.