局部搜索自适应核模糊聚类方法

核模糊C-均值聚类KFCM是利用核函数将数据映射到高维空间,通过计算数据点与聚类中心的隶属度对数据进行聚类的算法,拥有高效、快捷的特点而被广泛应用于各领域,然而KFCM算法存在对聚类中心的初始值敏感和不能自适应确定聚类数两个局限性。针对这两个问题,提出一种局部搜索自适应核模糊聚类方法,该方法引入核方法提高数据的可分性,并构造基于核函数的评价函数来确定最优的聚类数目和利用部分样本数据进行局部搜索以寻找初始聚类中心。人工数据和UCI数据集上的实验结果验证了该算法的有效性。

An adaptive kernel fuzzy clustering algorithm based on local search

Kernel Fuzzy C-Mean clustering (KFCM) utilizes kernel function to transform the data into the high-dimensional space, and uses the membership between data points and cluster centers to cluster the datasets. It is widely used in various fields due to its efficiency and fast speed. However, there are two limitations: the KFCM is sensitive to the initial values of clustering centers and it cannot automatically determine the number of clusters. Aiming at the two issues, we present an adaptive fuzzy clustering algorithm based on local search. The kernel method is introduced to improve the data separability, an evaluation index based on kernel is constructed to determine the number of clusters and the local search using small sample datasets is designed to look for the optimal cluster center. Experimental results on artificial datasets and the UCI dataset validate the effectiveness of the proposed method.