关键词: 核密度估计（KDE）, 抽样, 同步, 大样本, 聚类

Abstract: Since the existing clustering synchronization clustering algorithm Sync is highly complex in time, and it cannot be applied into the case of large sample, it proposes a new algorithm named Fast Clustering by Synchronization on Large Sample（FCSLS）. To apply this algorithm, it firstly condenses the large sample dataset by using the KDE based sampling method, and then, carries out the cluster synchronization of compressed dataset, finding out the best clustering data by using the Davies-Bouldin clustering criterion, finally, gets the final clustering results by clustering the rest objects in the large dataset. Based on the empirical result from the synthetic datasets and UCI real-world datasets, it concludes that FCSLS can detect clusters of any shape density and size without pre-setting the cluster number. Meanwhile, comparing with the compression algorithm based on RSDE and CCMEB, FCSLS can significantly reduce the operation time of the cluster synchronization algorithm without losing the clustering accuracy.

Key words: Kernel Density Estimate（KDE）, sampling, synchronization, large sample, clustering