按风格划分数据的模糊聚类算法

以K-means和模糊C均值为代表的划分式聚类算法无法有效处理按照风格为标准划分样本的聚类任务.针对此问题,文中提出按风格划分数据的模糊聚类算法.利用风格标准化矩阵表示包含在类簇中样本的风格信息,同时使用逼近标准风格之后的样本计算距离矩阵,并以隶属度表示样本点对于类簇的可代表程度.通过常用的交替优化策略同时优化隶属度矩阵和风格标准化矩阵.文中算法可以有效利用样本的风格信息和样本点与类簇之间的关系信息,在人工数据集和真实数据集上的实验表明算法的有效性.

A Fuzzy Style Clustering Algorithm on Stylistic Data

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.