Compressed K - Means for large-scale clustering
- Publication Type:
- Conference Proceeding
- Citation:
- 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2527 - 2533
- Issue Date:
- 2017-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| AAAI17.pdf | Published version | 1.8 MB |
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Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 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.
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