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

박동철 (명지대학교 정보공학과)
장병근 (명지대학교 정보공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.29, no.5C, 2004 , pp. 700-705 More about this Journal
Abstract
Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(PCM) 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.
Keywords
Neural Network; OCAEFCM; GBFCM;
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