{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:33:55Z","timestamp":1760236435594,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51939001 and 61976033"],"award-info":[{"award-number":["51939001 and 61976033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Social Science Foundation of China","award":["15CGL031"],"award-info":[{"award-number":["15CGL031"]}]},{"name":"Young Foundation of Ministry of Education Humanities and Social Sciences","award":["21YJC630066"],"award-info":[{"award-number":["21YJC630066"]}]},{"name":"the Liaoning Revitalization Talents Program","award":["XLYC1907084"],"award-info":[{"award-number":["XLYC1907084"]}]},{"name":"the Science &amp; Technology Innovation Funds of Dalian","award":["2018J11CY022"],"award-info":[{"award-number":["2018J11CY022"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["3132019353 and 3132021273"],"award-info":[{"award-number":["3132019353 and 3132021273"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a14120349","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T04:48:37Z","timestamp":1638247717000},"page":"349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement"],"prefix":"10.3390","volume":"14","author":[{"given":"Xutong","family":"Li","sequence":"first","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Taoying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.neucom.2019.03.060","article-title":"Multivariate time series clustering based on common principal component analysis","volume":"349","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1450018","DOI":"10.1142\/S012906571450018X","article-title":"A cluster merging method for time series microarray with production values","volume":"24","author":"Chira","year":"2014","journal-title":"Int. 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