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Article

GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
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Academic Editor: Frank Werner
Algorithms 2021, 14(12), 349; https://doi.org/10.3390/a14120349
Received: 15 November 2021 / Revised: 25 November 2021 / Accepted: 26 November 2021 / Published: 29 November 2021
Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results. View Full-Text
Keywords: two-dimensional image; Gramian Angular Difference Field; wavelet transform; deep embedded clustering two-dimensional image; Gramian Angular Difference Field; wavelet transform; deep embedded clustering
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MDPI and ACS Style

Li, X.; Li, T.; Wang, Y. GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement. Algorithms 2021, 14, 349. https://doi.org/10.3390/a14120349

AMA Style

Li X, Li T, Wang Y. GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement. Algorithms. 2021; 14(12):349. https://doi.org/10.3390/a14120349

Chicago/Turabian Style

Li, Xutong, Taoying Li, and Yan Wang. 2021. "GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement" Algorithms 14, no. 12: 349. https://doi.org/10.3390/a14120349

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