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A Deep Clustering Algorithm Based on Self-organizing Map Neural Network

  • Yanling Tao
  • Ying Li
  • Xianghong Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

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.

Keywords

Clustering algorithm Deep neural networks Stacked auto-encoders Self-organizing map neural network 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Social Development and Public AdministrationNorthwest Normal UniversityLanzhouChina
  2. 2.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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