Clustering of Incomplete Data Using Autoencoder and Fuzzy c-Means Algorithm 


Vol. 29,  No. 5, pp. 700-705, May  2004


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  Abstract

Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(FCM) is proposed in this paper. The proposed algorithm, called Optimal Completion Autoencoder Fuzzy c-Means(OCAEFCM), utilizes the Autoencoder Neural Network (AENN) and the Gradiant-based FCM (GBFCM) for optimal completion of missing data and clustering of the reconstructed data. The proposed OCAEFCM is applied to the IRIS data and a data set from a financial institution to evaluate the performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the OCAEFCM shows 18%~20% improvement of performance over OCSFCM.

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  Cite this article

[IEEE Style]

D. Park and B. Jang, "Clustering of Incomplete Data Using Autoencoder and Fuzzy c-Means Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 700-705, 2004. DOI: .

[ACM Style]

Dong-Chul Park and Bung-Geun Jang. 2004. Clustering of Incomplete Data Using Autoencoder and Fuzzy c-Means Algorithm. The Journal of Korean Institute of Communications and Information Sciences, 29, 5, (2004), 700-705. DOI: .

[KICS Style]

Dong-Chul Park and Bung-Geun Jang, "Clustering of Incomplete Data Using Autoencoder and Fuzzy c-Means Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 5, pp. 700-705, 5. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880