{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:24:16Z","timestamp":1773951856804,"version":"3.50.1"},"reference-count":62,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2021,10,4]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback\u2013Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder\u00a0+\u00a0k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-03-2021-0053","type":"journal-article","created":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T03:57:46Z","timestamp":1628308666000},"page":"686-701","source":"Crossref","is-referenced-by-count":10,"title":["CDEC: a constrained deep embedded clustering"],"prefix":"10.1108","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5924-6967","authenticated-orcid":false,"given":"Elham","family":"Amirizadeh","sequence":"first","affiliation":[]},{"given":"Reza","family":"Boostani","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"key2021100405235231400_ref001","article-title":"Clustering in the presence of side information: a non-linear approach","year":"2019","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2021100405235231400_ref002","first-page":"37","year":"2012"},{"key":"key2021100405235231400_ref003","article-title":"A hierarchical clustering of features approach for vehicle tracking in traffic environments","year":"2016","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2021100405235231400_ref004","volume-title":"Constrained Clustering: Advances in Algorithms, Theory, and Applications","year":"2008"},{"key":"key2021100405235231400_ref005","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","year":"2007","journal-title":"Data and Knowledge Engineering"},{"key":"key2021100405235231400_ref006","first-page":"127","year":"2012"},{"key":"key2021100405235231400_ref007","first-page":"1","article-title":"A semantic similarity measure based news posts validation on social media","year":"2018"},{"key":"key2021100405235231400_ref008","first-page":"3642","article-title":"Multi-column deep neural networks for image classification","year":"2012"},{"key":"key2021100405235231400_ref009","first-page":"1","article-title":"Transfer learning for Latin and Chinese characters with deep neural networks","year":"2012"},{"key":"key2021100405235231400_ref010","article-title":"A tutorial survey of architectures, algorithms, and applications for deep learning","year":"2014"},{"key":"key2021100405235231400_ref011","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1137\/S0895479896305696","article-title":"A multilinear singular value decomposition","volume":"21","year":"2000","journal-title":"SIAM Journal on Matrix Analysis and Applications"},{"key":"key2021100405235231400_ref012","article-title":"The organization of behavior","year":"1949","journal-title":"New York 1952 Donald The Organization of Behaviour 1952"},{"key":"key2021100405235231400_ref013","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.is.2006.10.006","article-title":"A local-density based spatial clustering algorithm with noise","volume":"32","year":"2007","journal-title":"Information Systems"},{"key":"key2021100405235231400_ref014","first-page":"47","article-title":"Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data","year":"2003"},{"key":"key2021100405235231400_ref015","unstructured":"Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996), \u201cA density-based algorithm for discovering clusters in large spatial databases with noise\u201d, in Kdd, pp. 226-231."},{"key":"key2021100405235231400_ref016","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.patcog.2018.12.015","article-title":"Autoencoder node saliency: selecting relevant latent representations","volume":"88","year":"2019","journal-title":"Pattern Recognition"},{"key":"key2021100405235231400_ref017","volume-title":"Introduction to Statistical Pattern Recognition","year":"2013"},{"key":"key2021100405235231400_ref018","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: a review","volume":"187","year":"2016","journal-title":"Neurocomputing"},{"key":"key2021100405235231400_ref019","first-page":"675","article-title":"Caffe: convolutional architecture for fast feature embedding","year":"2014"},{"key":"key2021100405235231400_ref020","first-page":"1094","year":"2011"},{"key":"key2021100405235231400_ref021","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key2021100405235231400_ref022","first-page":"2278","article-title":"Gradient-based learning applied to document recognition","year":"1998"},{"key":"key2021100405235231400_ref023","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","year":"1999","journal-title":"Nature"},{"key":"key2021100405235231400_ref024","first-page":"361","article-title":"RCV1: a new benchmark collection for text categorization research","volume":"5","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"key2021100405235231400_ref025","doi-asserted-by":"crossref","first-page":"103854","DOI":"10.1109\/ACCESS.2019.2929798","article-title":"Analysis of the terrorist organization alliance network based on complex network theory","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"key2021100405235231400_ref026","first-page":"1","article-title":"VDBSCAN: varied density based spatial clustering of applications with noise","year":"2007"},{"key":"key2021100405235231400_ref027","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","article-title":"LLNet: a deep autoencoder approach to natural low-light image enhancement","volume":"61","year":"2017","journal-title":"Pattern Recognition"},{"key":"key2021100405235231400_ref028","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"key2021100405235231400_ref029","first-page":"281","year":"1967"},{"key":"key2021100405235231400_ref030","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.patcog.2011.05.007","article-title":"A semi-supervised fuzzy clustering algorithm applied to gene expression data","volume":"45","year":"2012","journal-title":"Pattern Recognition"},{"key":"key2021100405235231400_ref031","unstructured":"Mart\u00edn Abadi, A.A., Barham, P., Brevdo, E., Chen, Z., Craig, C., Greg, S., Corrado, A.D., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Andrew, G., Harp, I., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.J.L., Man\u00e9, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M.J.S., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V.V.V., Vi\u00e9gas, F., Vinyals, O., Warden, P., Wattenberg, M., et al., \u201cTensorFlow: an end-to-end open source machine learning platform\u201d, available at: https:\/\/www.tensorflow.org\/."},{"key":"key2021100405235231400_ref032","article-title":"Distributed representations of words and phrases and their compositionality","year":"2013"},{"key":"key2021100405235231400_ref033","unstructured":"Nair, V. and Hinton, G.E. (2010), \u201cRectified linear units improve restricted Boltzmann machines\u201d, in Icml."},{"key":"key2021100405235231400_ref034","article-title":"An enhanced cosine-based visual technique for the robust tweets data clustering","year":"2021","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2021100405235231400_ref035","article-title":"Idea plagiarism detection with recurrent neural networks and vector space model","year":"2021","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2021100405235231400_ref036","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1109\/TNN.2011.2162000","article-title":"Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering","volume":"22","year":"2011","journal-title":"IEEE Transactions on Neural Networks"},{"key":"key2021100405235231400_ref037","first-page":"225","year":"2018"},{"key":"key2021100405235231400_ref038","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/BF00275687","article-title":"Simplified neuron model as a principal component analyzer","volume":"15","year":"1982","journal-title":"Journal of Mathematical Biology"},{"key":"key2021100405235231400_ref039","first-page":"3546","year":"2015"},{"key":"key2021100405235231400_ref040","article-title":"On vectorization of deep convolutional neural networks for vision tasks","year":"2015"},{"key":"key2021100405235231400_ref041","volume-title":"Learning Internal Representations by Error Propagation","year":"1985"},{"key":"key2021100405235231400_ref042","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.patcog.2017.01.002","article-title":"Deep learning and mapping based ternary change detection for information unbalanced images","volume":"66","year":"2017","journal-title":"Pattern Recognition"},{"key":"key2021100405235231400_ref043","first-page":"120","year":"1990"},{"key":"key2021100405235231400_ref044","first-page":"243","year":"2007"},{"key":"key2021100405235231400_ref045","first-page":"160","article-title":"MCD: mutually Connected Community Detection using clustering coefficient approach in social networks","year":"2019"},{"key":"key2021100405235231400_ref046","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TCSS.2019.2910818","article-title":"A novel stream clustering framework for spam detection in Twitter","volume":"6","year":"2019","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"key2021100405235231400_ref047","article-title":"Learning deep representations for graph clustering","year":"2014"},{"key":"key2021100405235231400_ref048","first-page":"384","year":"2009"},{"key":"key2021100405235231400_ref049","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"key2021100405235231400_ref050","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","year":"2007","journal-title":"Statistics and Computing"},{"key":"key2021100405235231400_ref051","first-page":"3371","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"key2021100405235231400_ref052","first-page":"1","year":"2006"},{"key":"key2021100405235231400_ref053","first-page":"577","article-title":"Clustering with instance-level constraints","volume":"1097","year":"2000","journal-title":"AAAI\/IAAI"},{"key":"key2021100405235231400_ref054","volume-title":"Intelligent Clustering with Instance-Level Constraints","year":"2002"},{"key":"key2021100405235231400_ref055","first-page":"577","year":"2001"},{"key":"key2021100405235231400_ref056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10618-012-0291-9","article-title":"On constrained spectral clustering and its applications","volume":"28","year":"2014","journal-title":"Data Mining and Knowledge Discovery"},{"key":"key2021100405235231400_ref057","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.patcog.2016.10.022","article-title":"A coupled hidden Markov random field model for simultaneous face clustering and tracking in videos","volume":"64","year":"2017","journal-title":"Pattern Recognition"},{"key":"key2021100405235231400_ref058","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","year":"2016"},{"key":"key2021100405235231400_ref059","first-page":"907","article-title":"Fast approximate spectral clustering","year":"2009"},{"key":"key2021100405235231400_ref060","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1109\/TIP.2010.2049235","article-title":"Image clustering using local discriminant models and global integration","volume":"19","year":"2010","journal-title":"IEEE Transactions on Image Processing"},{"key":"key2021100405235231400_ref061","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.knosys.2011.08.011","article-title":"Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems","volume":"32","year":"2012","journal-title":"Knowledge-Based Systems"},{"key":"key2021100405235231400_ref062","article-title":"SOM approach for clustering customers using credit card transactions","year":"2019","journal-title":"International Journal of Intelligent Computing and Cybernetics"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-03-2021-0053\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-03-2021-0053\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:11Z","timestamp":1753397651000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/14\/4\/686-701\/135405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,10]]},"references-count":62,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,8,10]]},"published-print":{"date-parts":[[2021,10,4]]}},"alternative-id":["10.1108\/IJICC-03-2021-0053"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-03-2021-0053","relation":{},"ISSN":["1756-378X"],"issn-type":[{"value":"1756-378X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,8,10]]}}}