Abstract
Clustering is one of the most basic unsupervised learning problems in the field of machine learning and its main goal is to separate data into clusters with similar data points. Because of various redundant and complex structures for the raw data, the general algorithm usually is difficult to separate different clusters from the data and the effect is not obvious. Deep learning is a technology that automatically learns nonlinear and more conducive clustering features from complex data structures. This paper presents a deep clustering algorithm based on self-organizing map neural network. This method combines the feature learning ability of stacked auto-encoder from the raw data and feature clustering with unsupervised learning of self-organizing map neural network. It is aim to achieve the greatest separability for the data space. Through the experimental analysis and comparison, the proposed algorithm has better recognition rate, and improves the clustering performance on low and high dimension data.
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Acknowledgment
The work is supported by the National Natural Science Foundation of China under Grant No. 61762080, and the Medium and Small Scale Enterprises Technology Innovation Foundation of Gansu Province under Grant No. 17CX2JA038.
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Tao, Y., Li, Y., Lin, X. (2018). A Deep Clustering Algorithm Based on Self-organizing Map Neural Network. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_20
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DOI: https://doi.org/10.1007/978-3-319-95957-3_20
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