Compressed K-Means for Large-Scale Clustering

Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering. Specifically, high-dimensional data are compressed into short binary codes, which are well suited for fast clustering. CKM enjoys two key benefits: 1) storage can be significantly reduced by representing data points as binary codes; 2) distance computation is very efficient using Hamming metric between binary codes. We propose to jointly learn binary codes and clusters within one framework. Extensive experimental results on four large-scale datasets, including two million-scale datasets demonstrate that CKM outperforms the state-of-theart large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.

Authors

• Xiaobo Shen Nanjing University of Science and Technology
• Weiwei Liu Wuhan University
• Ivor Tsang Centre for Frontier AI Research, Agency for Science, Technology and Research Institute of High Performance Computing, Agency for Science, Technology and Research
• Fumin Shen University of Electronic Science and Technology of China
• Quan-Sen Sun

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