Space Structure Based Affinity Propagation Algorithm for Categorical Data
WANG Qi, QIAN Yuhua, LI Feijiang
School of Computer and Information Technology, Shanxi University, Taiyuan 030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：Constructing a reasonable similarity measure is difficult due to the lack of clear space structure in categorical data. Therefore, numerical clustering algorithms can hardly be extended to categorical data clustering. In this paper, a representation method for transforming the categorical data into numerical data is introduced. The similarity between samples is reconstructured and the structure feature of the original categorical data is maintained in the reconstruction process. Based on the data representation method, the affinity propagation(AP) clustering algorithm is migrated to the categorical data clustering. A space structure based AP algorithm for categorical data(SBAP) is proposed. Experimental results on several categorical datasets from the UCI dataset show that the proposed method makes AP algorithm deal with the categorical data clustering problem effectively with a significant improvement in performance.
王齐，钱宇华，李飞江. 基于空间结构的符号数据仿射传播算法*[J]. 模式识别与人工智能, 2016, 29(12): 1132-1139.
WANG Qi, QIAN Yuhua, LI Feijiang. Space Structure Based Affinity Propagation Algorithm for Categorical Data. , 2016, 29(12): 1132-1139.
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