引用本文:邱保志,张瑞霖,李向丽.基于过滤模型的聚类算法[J].控制与决策,2020,35(5):1091-1101
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基于过滤模型的聚类算法
邱保志,张瑞霖,李向丽
(郑州大学信息工程学院,郑州450001)
摘要:
合理的聚类原型是正确聚类的前提.针对现有聚类算法原型选取不合理、计算聚类个数存在偏差等问题,提出基于过滤模型的聚类算法(CA-FM).算法以提出的过滤模型去除干扰聚类过程的边界和噪声对象,依据核心对象之间的近邻关系生成邻接矩阵,通过遍历矩阵计算聚类个数;然后,按密度因子将数据对象排序,从中选出聚类原型;最后,将其余对象按照距高密度对象的最小距离划分到相应的簇中,形成最终聚类.在人工合成数据集、UCI数据集以及人脸识别数据集上的实验结果验证了算法的有效性,与同类算法相比,CA-FM算法具有较高的聚类精度.
关键词:  聚类算法  过滤模型  偏差因子  聚类原型  局部密度  密度因子
DOI:10.13195/j.kzyjc.2018.1089
分类号:TP273
基金项目:河南省基础与前沿技术研究项目(152300410191).
Clustering algorithm based on filter model
QIU Bao-zhi,ZHANG Rui-lin,LI Xiang-li
(School of Information Engineering,Zhengzhou University,Zhengzhou450001,China)
Abstract:
Reasonable clustering prototype is the premise of correct clustering. Most of the existing clustering algorithms have some shortcomings such as the unreasonable selection of clustering prototypes and calculation deviation of cluster numbers. A clustering algorithm based on filter model (CA-FM) is proposed. The algorithm uses the proposed filtering model to remove the boundary and noise objects which interfere with the clustering process. The adjacency matrix is generated according to the neighbor relationships among the core objects, and the number of clusters is calculated by traversing the matrix. Then, the objects are sorted according to the density factor, and clustering prototypes are selected from them. Finally, the remaining objects are assigned into corresponding clusters according to the minimum distance from the high density objects. The effectiveness of the proposed algorithm is demonstrated by experiments on synthetic datasets, UCI datasets and Olivetti face dataset. Compared with similar algorithms, the CA-FM has a higher clustering accuracy.
Key words:  clustering algorithm  filter model  deviation factor  clustering prototype  local density  density factor

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