Abstract
Clustering is an important research content for data analysis. It can obtain the underlying structure of data from the original data set. Clustering on massive high-dimensional data sets is still a huge challenge. In this paper, we propose a clustering ensemble algorithm based on BLB and stratified sampling framework for massive high-dimensional data. From two aspects of sample and feature, we use the BLB (Bag of Little Bootstrap) algorithm to divide the original data set into several small-scale data subsets, then use feature stratified sampling to obtain a low-dimensional subset. we generate base clustering results on multiple small-scale low-dimensional subsets, and finally obtain a cluster integration results by link-based consensus functions. The experimental results on synthetic data sets and UCI real data sets show that the algorithm proposed in this paper is effective for clustering massive high-dimensional data.
Published Version
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