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Open AccessArticle

Double Deep Autoencoder for Heterogeneous Distributed Clustering

1
Department of Business Administration, Chung Yuan Christian University, Taoyuan 32023, Taiwan
2
Department of Computer Science and Information Management, Soochow University, Taipei 10048, Taiwan
*
Author to whom correspondence should be addressed.
Information 2019, 10(4), 144; https://doi.org/10.3390/info10040144
Received: 4 March 2019 / Revised: 15 April 2019 / Accepted: 15 April 2019 / Published: 17 April 2019
(This article belongs to the Section Artificial Intelligence)
Given the issues relating to big data and privacy-preserving challenges, distributed data mining (DDM) has received much attention recently. Here, we focus on the clustering problem of distributed environments. Several distributed clustering algorithms have been proposed to solve this problem, however, previous studies have mainly considered homogeneous data. In this paper, we develop a double deep autoencoder structure for clustering in distributed and heterogeneous datasets. Three datasets are used to demonstrate the proposed algorithms, and show their usefulness according to the consistent accuracy index. View Full-Text
Keywords: distributed data mining (DDM); clustering; big data; heterogeneous databases; deep autoencoder distributed data mining (DDM); clustering; big data; heterogeneous databases; deep autoencoder
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Chen, C.-Y.; Huang, J.-J. Double Deep Autoencoder for Heterogeneous Distributed Clustering. Information 2019, 10, 144.

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