Fuzzy Clustering Data Arrays with Omitted Observations

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Zhengbing Hu 1,* Yevgeniy V. Bodyanskiy 2 Oleksii K. Tyshchenko 2 Vitalii M. Tkachov 2

1. School of Educational Information Technology, Central China Normal University, Wuhan, China

2. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.06.03

Received: 20 Sep. 2016 / Revised: 15 Jan. 2017 / Accepted: 2 Mar. 2017 / Published: 8 Jun. 2017

Index Terms

Computational Intelligence, Machine Learning, missing values, gaps' recovery, adaptive system, fuzzy clustering


An adaptive neural system which solves a problem of clustering data with missing values in an online mode with a permanent correction of restorable table elements and clusters’ centroids is proposed in this article. The introduced neural system is characterized by both a high speed and a simple numerical implementation. It can process information in a real-time mode.

Cite This Paper

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Vitalii M. Tkachov,"Fuzzy Clustering Data Arrays with Omitted Observations", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.6, pp.24-32, 2017. DOI:10.5815/ijisa.2017.06.03


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