先通过数据约简技术在不损失数据聚类结构的前提下对数据进行精简,利用提出的近似模糊c均值聚类算法对精简后数据进行划分得到初始化中心,再在该中心基础上通过模糊c均值聚类算法结合聚类有效性指标,实现对数据的无监督聚类,改进了无监督模糊c均值聚类算法聚类性能过分依赖初始化中心及大数据集下计算效率不理想的问题.与已有算法的对比实验表明,所提出的算法具有更高的求解精度与计算效率,得到的聚类个数更合理.%On the condition of losing less information and retaining less data, the data were refined by the data reduction technique. The proposed approximation algorithm for fuzzy omeans clustering was used to estimate the cluster centers. Combined with validity indexed and estimated centers, FCM can execute unsupervised clustering. The proposed algorithm improved the computational efficiency and performance of the conventional unsupervised fuzzy c-means clustering algorithm. The contrast experimental results with conventional algorithms show that the proposed algorithm has a relatively high precision and efficiency. It can obtain the cluster number more accurately than the conventional algorithm.
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