A Fuzzy Style Clustering Algorithm on Stylistic Data
SHEN Hao1,2 , WANG Shitong1,2
1.School of Digital Media, Jiangnan University, Wuxi 214122 2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122
Abstract In the stylistic data, different organizational styles obviously or implicitly exist in different clusters. The classical partitional clustering methods represented by K-means and fuzzy C-means are ineffective for the stylistic data. Therefore, a fuzzy style clustering(FSC) is proposed. A style normalization matrix is utilized to represent the style information of the samples within each cluster, and the distance matrix is calculated with samples transformed by style normalization matrices. Besides, the fuzzy membership is exploited to describe the representable degree of a sample for a certain cluster. The membership matrix and style normalization matrix are optimized simultaneously by the commonly-used alternating optimization technique. FSC can make use of the style information of samples and the information between samples and clusters effectively, and the experimental results on synthetic and real datasets indicate the effectiveness of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China (No.61572236)
Corresponding Authors:
WANG Shitong, Ph.D., professor. His research interests include pattern recognition, artificial intelligence, machine learning and deep learning.
About author:: (SHEN Hao, master student. His research interests include artificial intelligence and machine learning.)
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2019, Vol. 32 
