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An Adaptive Density Data Stream Clustering Algorithm

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Abstract

Now we are in the age of big data. Huge amount of data and information are generated every time. Traditional data stream algorithms are suit for the data streams with low dimension and simple structure. However, with the development of information technology, the produced data streams are becoming more and more complicated. It is particularly important to study how to find new associations and patterns from complex data to achieve the cognition ability and judgment ability like human brain. Clustering data streams with mixed attributes of irregular distribution is a big challenge in data mining. To solve this problem, we present an adaptive density data stream clustering algorithm—ADStream. ADStream is based on the online–off-line clustering framework. It can automatically recognize the initial clusters by passing messages between data points. Then a novel time-decay density clustering strategy is designed to group and update the continuously arriving data streams. Comprehensive experimental results demonstrate that ADStream is adaptive to the evolving data streams and may generate high-quality clusters with fast processing rate.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61379101), and the National Key Basic Research Program of China (No. 2013CB329502).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Jian Zhang, Hongjie Jia & Jun Qian

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding, Jian Zhang & Hongjie Jia

Authors
  1. Shifei Ding
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  2. Jian Zhang
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  3. Hongjie Jia
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  4. Jun Qian
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Correspondence to Shifei Ding.

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Ding, S., Zhang, J., Jia, H. et al. An Adaptive Density Data Stream Clustering Algorithm. Cogn Comput 8, 30–38 (2016). https://doi.org/10.1007/s12559-015-9342-z

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  • DOI: https://doi.org/10.1007/s12559-015-9342-z

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