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Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clustering

  • Tianyue Zhang
  • Baile Xu
  • Furao Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)

Abstract

In this paper, a neural network named fuzzy self-organizing incremental neural network (fuzzy SOINN) is presented for fuzzy clustering with following four characteristics: fuzzy, incremental learning, topological representation and resistance to noise. No predefined structures of clusters is required due to the self-adjusting nodes and edges which fit the learning data incrementally. A removal of nodes and edges promises the robustness of the network to the noisy data. Experiments on artificial and real-world data prove the validity of the clustering method.

Keywords

Fuzzy clustering Incremental or online learning Topological representation Self-organizing incremental neural network (SOINN) 

Notes

Acknowledgments

This work is supported in part by the National Science Foundation of China under Grant Nos. (61373130, 61375064, 61373001), and Jiangsu NSF grant (BK20141319).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing UniversityNanjingChina

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