基于局部和全局信息的改进聚类算法

Improved Clustering Algorithm Based on Local and Global Information

传统K-means算法在随机选取初始聚类中心时,容易导致结果不稳定,谱聚类算法直接在相似矩阵上进行分割,对结果的准确性影响较大,而局部和全局正则化聚类算法未考虑数据空间分布对结果的影响。为此,引入离散度矩阵对局部和全局正则化聚类算法进行改进。改进算法考虑数据的分布信息,通过在局部信息目标函数中引入离散度矩阵,结合全局信息的目标函数,将目标函数最小化问题转换为分解稀疏矩阵特征的问题。在UCI机器学习数据集和公共数据挖掘数据集上的实验结果表明,与K-means及标准谱聚类算法相比,该算法的预测精度更高。

Traditional K-means clustering algorithm is sensitive to the initialization. Spectral clustering operates on the similar matrix,and severely affects the cluster result. Clustering with local and global regularization does not take the distribution of data set into consideration. To solve this problem,this paper introduces the dispersion matrix to improve the clustering on the base of local and global regularization. The proposed algorithm takes the distribution of data set into consideration which combines the local information and dispersion matrix. The global optimal information is considered, and then it gets the final optimization problem which can be solved by the eigenvalue decomposition of a spare symmetric matrix. Several mentioned algorithms are tested on UCI machine learning data sets and public data mining data sets. Experimental results and comparison results show the greater performance of the proposed algorithm.