摘要
采用2个不同的谱聚类算法解决文本聚类集成问题。为使算法可扩展到大规模应用,基于代数变换,通过求解小规模矩阵的特征值分解问题避免了大规模矩阵的特征值分解问题,有效降低了2个谱聚类算法的计算复杂度。分别从矩阵扰动理论和图上的随机游走的角度解释了2个算法的有效性。在真实文本集上的实验结果表明:提出的代数变换方法是有效的,该方法可以有效提高谱聚类算法的运行效率;该聚类集成谱算法比其他常见的聚类集成算法更优越、更高效,可以有效解决文本聚类集成问题。
Two spectral clustering algorithms were brought into document cluster ensemble problem.To make the algorithms extensible to large scale applications,the large scale matrix eigenvalue decomposition was avoided by solving the eigenvalue decomposition of two induced small matrixes,and thus computational complexity of the algorithms was effectively reduced.Experiments on real-world document sets show that the algebraic transformation method is feasible for it could effectively increase the efficiency of spectral algorithms;both of the proposed cluster ensemble spectral algorithms are more excellent and efficient than other common cluster ensemble techniques,and they provide a good way to solve document cluster ensemble problem.
出处
《通信学报》
EI
CSCD
北大核心
2010年第6期58-66,共9页
Journal on Communications
基金
国家自然科学基金资助项目(60603092
60903082
60975042)
高等学校博士学科点专项科研基金资助项目(20070217043)~~
关键词
聚类集成
文本聚类
谱聚类
矩阵扰动理论
图上的随机游动
cluster ensemble
document clustering
spectral clustering
matrix perturbation theory
random walk on graph
