中文摘要：

聚类集成技术可有效提高单聚类算法的精度和稳定性，其中的关键问题是如何根据不同的聚类成员组合为更好的聚类结果．文中引入谱聚类算法解决文本聚类集成问题，设计基于正则化拉普拉斯矩阵的谱算法（NLMSA）．该算法基于代数变换，通过求解小规模矩阵的特征值和特征向量间接获得正则化拉普拉斯矩阵的特征向量，并用于后续聚类．进一步研究谱聚类算法的关键思想，设计基于超边转移概率矩阵的谱算法（HTMSA）．该算法通过求解超边的低维嵌入间接获得文本的低维嵌入，并用于后续K均值算法．在TREC和Reuters文本集上的实验结果验证NLMSA和HTMSA的有效性，它们都获得比其它基于图划分的集成算法更为优越的结果．HTMSA获得的结果比NLMSA略差，而时间和空间需求则比NLMSA低得多．

英文摘要：

Cluster ensemble techniques are effective in improving both the robustness and the stability of the single clustering algorithm. How to combine multiple clusters to yield a final superior clustering result is critical in cluster ensemble. Spectral clustering algorithm is introduced to solve document cluster ensemble problem. Normalized Laplacian matrix-based spectral algorithm （NLMSA） is proposed. According to algebraic transformation, it computes eigenvalues and eigenvectors of a small matrix to obtain the eigenvectors of normalized Laplacian matrix. The key idea of spectral clustering algorithm is further investigated, and hyperedge transition matrix-based spectral algorithm （HTMSA） is attains the low dimensional embeddings of documents by those of hyperedges and then proposed. It the K-means algorithm is used to cluster according to those embedding results of documents. Experimental results on TREC and Reuters document sets demonstrate the effectiveness of the proposed algorithms. Both NLMSA and HTMSA outperform other cluster ensemble techniques based on graph partitioning. NLMSA obtains better results than HTMSA while the computational cost of HTMSA is much lower than that of NLMSA.