Abstract
Spectral clustering is a flexible clustering algorithm that can produce high-quality clusters on small scale data sets, but it is limited applicable to large scale data sets because it needs O(n 3) computational operations to process a data set of n data points[1]. Based on the minimization of the increment of distortion, we tackle this problem by developing a novel efficient growing vector quantization method to preprocess a large scale data set, which can compress the original data set into a small set of representative data points in one scan of the original data set. Then we apply spectral clustering algorithm to the small set. Experiments on real data sets show that our method provides fast and accurate clustering results.
This work is supported by National Natural Science Foundation of China under Grant No. 61003311, Jiangsu Provincial Key Laboratory of Network and Information Security Grant No. BM2003201-201006, Anhui Provincial Natural Science Research Key Project of China under Grant No. KJ2011A040, Youth Foundation of Anhui University of Technology under Grant No. QZ201316.
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Wang, X., Zheng, X., Qin, F., Zhao, B. (2013). A Fast Spectral Clustering Method Based on Growing Vector Quantization for Large Data Sets. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_3
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DOI: https://doi.org/10.1007/978-3-642-53917-6_3
Publisher Name: Springer, Berlin, Heidelberg
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