Spectral Clustering Algorithm Based on Mixed Data Similarity Measure
MA Heng1，DING Shi-fei(1，2)
1（School of Computer Science and Technology，China University of Mining and Technology，Xuzhou 221116,China）2（Key Laboratory of Intelligent Information Processing，Institute of Computing Technology，Chinese Academy of Sciences，Beijing 100190,China）
Abstract：With the development of the technology，people have produced a large amount of data in the life，some of these data with numerical and categorical two types of attributes.Most existing clustering algorithms can only deal with the data has one type attribute，they are often difficult to deal with mixed attribute data.To solve this problem，this paper proposes a spectral clustering algorithm based on mixed data similarity measure，First build the dissimilarity measure for two kind of attribute data respectively，Then base on the dissimilarity measure，establish the similarity relationship between the mixed data with a similarity measure，let one mixture data as an undirected graph vertex，the similarity relations between the mixed data are mapped into the undirected edge weight between two vertices，Finally through Spectral clustering to achieve clustering of mixed data.Selected several mixed data set for experiment from UCI standard data sets，and compared with other clustering algorithm for mixed data，Verified this algorithm is effective for mixed data clustering.
马恒1，丁世飞(1，2). 一种基于混合数据相似性度量的谱聚类算法[J]. 小型微型计算机系统, 2016, 37(8): 1746-1750.
MA Heng1，DING Shi-fei(1，2). Spectral Clustering Algorithm Based on Mixed Data Similarity Measure. Journal of Chinese Computer Systems, 2016, 37(8): 1746-1750.
2016, Vol. 37 
