Relevant bibliographies by topics / Deep Embedded Clustering

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  3. Conference papers

Academic literature on the topic 'Deep Embedded Clustering'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Deep Embedded Clustering"

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Miklautz, Lukas, Dominik Mautz, Muzaffer Can Altinigneli, Christian Böhm, and Claudia Plant. "Deep Embedded Non-Redundant Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5174–81. http://dx.doi.org/10.1609/aaai.v34i04.5961.

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Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple
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Wada, Yuichiro, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, and Takafumi Kanamori. "Spectral Embedded Deep Clustering." Entropy 21, no. 8 (2019): 795. http://dx.doi.org/10.3390/e21080795.

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We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by u
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Ren, Yazhou, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, and Zenglin Xu. "Semi-supervised deep embedded clustering." Neurocomputing 325 (January 2019): 121–30. http://dx.doi.org/10.1016/j.neucom.2018.10.016.

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Chen, Zhikui, Chaojie Li, Jing Gao, Jianing Zhang, and Peng Li. "Semisupervised Deep Embedded Clustering with Adaptive Labels." Scientific Programming 2021 (January 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/6613452.

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Deep embedding clustering (DEC) attracts much attention due to its outperforming performance attributed to the end-to-end clustering. However, DEC cannot make use of small amount of a priori knowledge contained in data of increasing volume. To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori knowledge. Specifically, a deep semisupervised clustering network is designed based on the autoencoder paradigm and deep clustering, which well mine the clu
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Mautz, Dominik, Claudia Plant, and Christian Böhm. "DeepECT: The Deep Embedded Cluster Tree." Data Science and Engineering 5, no. 4 (2020): 419–32. http://dx.doi.org/10.1007/s41019-020-00134-0.

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Abstract The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to
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Cahyadi, Danu Julian, Hendri Murfi, Yudi Satria, Sarini Abdullah, and Yekti Widyaningsih. "Analisis Performa Deep Embedded Clustering untuk Pendeteksian Topik." Techno.Com 24, no. 1 (2025): 56–67. https://doi.org/10.62411/tc.v24i1.11841.

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Pendeteksian topik adalah solusi untuk mengungkap struktur laten dalam sebuah dokumen. Kerangka umum pendeteksian topik berbasis clustering terdiri dari dua langkah: pembelajaran representasi dan pendeteksian topik melalui clustering. Dalam penelitian ini, Bidirectional Encoder Representations from Transformers (BERT) digunakan untuk pembelajaran representasi karena BERT mampu menangkap konteks setiap kata berdasarkan kata-kata di sekitarnya. Representasi teks yang diperoleh dari BERT digunakan untuk pendeteksian topik dengan clustering. Deep Embedded Clustering (DEC) dan Improved DEC (IDEC) a
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Ozanich, Emma, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, and Simon Freeman. "Deep embedded clustering of coral reef bioacoustics." Journal of the Acoustical Society of America 149, no. 4 (2021): 2587–601. http://dx.doi.org/10.1121/10.0004221.

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Reeves Ozanich, Emma, Aaron M. Thode, Peter Gerstoft, Lauren A. Freeman, and Simon E. Freeman. "Deep embedded clustering of coral reef bioacoustics." Journal of the Acoustical Society of America 149, no. 4 (2021): A113. http://dx.doi.org/10.1121/10.0004685.

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Zhou, Wen'An, and Qiang Zhou. "Deep Embedded Clustering With Adversarial Distribution Adaptation." IEEE Access 7 (2019): 113801–9. http://dx.doi.org/10.1109/access.2019.2935388.

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Gozet, Melisa, Mehmet Karakose, and Asim Egemen Yilmaz. "Deep embedded clustering using crowd density map." IET Conference Proceedings 2024, no. 37 (2025): 758–63. https://doi.org/10.1049/icp.2025.0888.

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Book chapters on the topic "Deep Embedded Clustering"

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Le, Ngoc-Toan, and Thanh-Hieu Bui. "Occupation Clustering Using Deep Embedded Kmeans." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-583-6_2.

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Song, Kang, Wei Han, Chamara Kasun Liyanaarachchi Lekamalage, and Lihui Chen. "Deep Embedded Clustering with Random Projection Penalty." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_17.

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Kolla, Morarjee, and T. Venugopal. "Concatenated Global Average Pooled Deep Convolutional Embedded Clustering." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1420-3_84.

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Jothi, R., and K. Muthukumaran. "Telecom Customer Segmentation Using Deep Embedded Clustering Algorithm." In Machine Learning and Data Analytics for Solving Business Problems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18483-3_5.

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Chen, Tianzhen, and Wei Sun. "Deep Convolutional Embedded Fuzzy Clustering with Wasserstein Loss." In Artificial Intelligence in Data and Big Data Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_14.

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Jothi, R. "Wearable Fall-Detection Using Deep Embedded Clustering Algorithm." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6332-1_69.

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Chen, Jiawen, Zhang Zhengwei, Suying Wang, and Rui Shi. "Clustering of Daily Load Curve Based on Improved Deep Embedded Clustering Algorithm." In Proceedings of the 7th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0063-3_60.

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Zhang, Kai, Zheng Lian, Jiangmeng Li, Haichang Li, and Xiaohui Hu. "Short Text Clustering with a Deep Multi-embedded Self-supervised Model." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86383-8_12.

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Tirambulo, Coco Victoria, Simona Merlini, Carlos Lizárraga-Celaya, Mithun Paul, Roberta Diaz Brinton, and Francesca Vitali. "Unsupervised Deep Embedded Clustering Reveals High-Risk Subgroups for Alzheimer’s Disease." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95841-0_76.

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Khoo, Hui Wen, Yin Hoe Ng, and Chee Keong Tan. "A Fast and Precise Indoor Positioning System Based on Deep Embedded Clustering." In Proceedings of the Multimedia University Engineering Conference (MECON 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-082-4_6.

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Conference papers on the topic "Deep Embedded Clustering"

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Cahyadi, Danu Julian, Hendri Murfi, Yudi Satria, Sarini Abdullah, and Yekti Widyaningsih. "BERT-Based Deep Embedded Clustering for Topic Modeling." In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2024. http://dx.doi.org/10.1109/ic3ina64086.2024.10732729.

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Zuo, Mingcheng, Fujian Xu, and Dunwei Gong. "A Deep Embedded Clustering with Selective Data Augmentation." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865700.

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Ibrahim, Omar A., Jianxi Wang, Marek Z. Reformat, Petr Musilek, and James C. Bezdek. "Generalized Deep Embedded Fuzzy C-Means for Clustering High-Dimensional Data." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611833.

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Wang, Zhencheng, and Dan Li. "Structure-preserving deep embedded clustering algorithm for incomplete gene expression data." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661868.

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Waghale, Pratibha, and Lalit Damahe. "Integrating Deep Embedded Clustering with DeepLabV3 for Effective Camouflaged Object Detection." In 2024 IEEE Pune Section International Conference (PuneCon). IEEE, 2024. https://doi.org/10.1109/punecon63413.2024.10894871.

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Yates, Don, Hakki Erhan Sevil, Arash Mahyari, and David Gray. "Deep embedded multiview object clustering using aerial images in the wild." In Automatic Target Recognition XXXV, edited by Kenny Chen, Timothy L. Overman, and Riad I. Hammoud. SPIE, 2025. https://doi.org/10.1117/12.3053614.

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Li, Xiang, Yu Tang, Erkang Li, and Di Lin. "Unsupervised Identification Method of Radio-Frequency Fingerprint Based on Deep Clustering." In 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES). IEEE, 2024. https://doi.org/10.1109/icites62688.2024.10777458.

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Mahendar, Maragoni, Pinnapureddy Manasa, Satyanarayana Nimmala, K. Raghavendar, Anjum Nabi Sheikh, and Medikonda Asha Kiran. "Dynamic Tumor Analysis Using Deep Embedded Clustering (DEC) for Personalized Oncology Treatment." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968766.

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Ma, Ying, and Qianlong Wu. "Optimizing Emergency Medical Supplies Dispatch Using Deep Embedded Clustering and PSO During Public Health Crises." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774533.

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See, Kai-Jun, Chee-Ming Ting, Fuad Noman, et al. "Deep Multi-Graph Embedded Clustering for Community Detection in FMRI Functional Brain Networks Across Individuals." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647708.

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