{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:50:54Z","timestamp":1780084254714,"version":"3.54.0"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.<\/jats:p>","DOI":"10.3390\/a16010028","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T02:15:42Z","timestamp":1672798542000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering"],"prefix":"10.3390","volume":"16","author":[{"given":"Deepesh","family":"Chugh","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Netaji Subhas University of Technology, New Delhi 110078, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Himanshu","family":"Mittal","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Sciences, Indira Gandhi Delhi Technical University for Women, New Delhi 110006, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Guru Ghasidas University, Bilaspur 495009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9905-2270","authenticated-orcid":false,"given":"Ritu","family":"Chauhan","sequence":"additional","affiliation":[{"name":"Centre for Computational Biology and Bioinformatics, Amit University, Noida 201301, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3144-0784","authenticated-orcid":false,"given":"Eiad","family":"Yafi","sequence":"additional","affiliation":[{"name":"School of Computer Science, FEIT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7745-9667","authenticated-orcid":false,"given":"Mukesh","family":"Prasad","sequence":"additional","affiliation":[{"name":"School of Computer Science, FEIT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.knosys.2015.05.014","article-title":"Recent advances and emerging challenges of feature selection in the context of big data","volume":"86","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","unstructured":"Bellman, R. (1957). Dynamic Programming, Princeton University Press."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Keogh, E., and Mueen, A. (2017). Curse of Dimensionality. Encyclopedia of Machine Learning and Data Mining, Springer.","DOI":"10.1007\/978-1-4899-7687-1_192"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_5","first-page":"1157","article-title":"An Introduction of Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res. Spec. Issue Var. Feature Sel."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","article-title":"A review of microarray datasets and applied feature selection methods","volume":"282","author":"Herrera","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_7","first-page":"1289","article-title":"An extensive empirical study of feature selection metrics for text classification","volume":"3","author":"Forman","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1007\/11861898_30","article-title":"Feature Selection for Automatic Image Annotation","volume":"2","author":"Setia","year":"2006","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/TSMC.2015.2406855","article-title":"An Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithm","volume":"45","author":"Lin","year":"2015","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TFUZZ.2002.1006431","article-title":"Fuzzy logic approaches to structure preserving dimensionality reduction","volume":"10","author":"Pal","year":"2002","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/5326.897072","article-title":"Neural networks for classification: A survey","volume":"30","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.patrec.2013.12.008","article-title":"Integration of dense subgraph finding with feature clustering for unsupervised feature selection","volume":"40","author":"Bandyopadhyay","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mittal, H., Saraswat, M., Bansal, J., and Nagar, A. (2020, January 1\u20134). Fake-Face Image Classification using Improved Quantum-Inspired Evolutionary-based Feature Selection Method. Proceedings of the IEEE Symposium Series on Computational Intelligence, Canberra, Australia.","DOI":"10.1109\/SSCI47803.2020.9308337"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. (2006). Feature Extraction: Foundations and Applications, Springer.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8520","DOI":"10.1016\/j.eswa.2015.07.007","article-title":"Feature selection using Joint Mutual Information Maximisation","volume":"42","author":"Bennasar","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mandal, M., and Mukhopadhyay, A. (2013). Unsupervised Non-redundant Feature Selection: A Graph-Theoretic Approach. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-642-35314-7_43"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107663","DOI":"10.1016\/j.patcog.2020.107663","article-title":"Pairwise dependence-based unsupervised feature selection","volume":"111","author":"Lim","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cai, D., Zhang, C., and He, X. (2010, January 24\u201328). Unsupervised feature selection for multi-cluster data. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining-KDD \u201810, Washington, DC, USA.","DOI":"10.1145\/1835804.1835848"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2016.09.043","article-title":"Unsupervised feature selection via Diversity-induced Self-representation","volume":"219","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.patcog.2014.08.006","article-title":"Unsupervised feature selection by regularized self-representation","volume":"48","author":"Zhu","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.1109\/TFUZZ.2020.3016339","article-title":"A New Fuzzy Cluster Validity Index for Hyperellipsoid or Hyperspherical Shape Close Clusters with Distant Centroids","volume":"29","author":"Mittal","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1049\/el.2017.2476","article-title":"Efficient information-theoretic unsupervised feature selection","volume":"54","author":"Lee","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_24","unstructured":"Han, D., and Kim, J. (2015, January 7\u201312). Unsupervised Simultaneous Orthogonal basis Clustering Feature Selection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_25","first-page":"1","article-title":"An information-theoretic graph-based approach for feature selection","volume":"45","author":"Das","year":"2019","journal-title":"S\u0101dhan\u0101"},{"key":"ref_26","unstructured":"He, X., Cai, D., and Niyogi, P. (2005, January 5\u20138). Laplacian Score for Feature Selection. Proceedings of the 18th International Conference on Neural Information Processing Systems 2005, Vancouver, BA, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1109\/34.990133","article-title":"Unsupervised feature selection using feature similarity","volume":"24","author":"Mitra","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Dua, D., and Graff, C. (2019). UCI Machine Learning Repository, University of California, School of Information and Computer Science. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gakii, C., Mireji, P.O., and Rimiru, R. (2022). Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets. Algorithms, 15.","DOI":"10.3390\/a15010021"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.eswa.2017.06.032","article-title":"A new hybrid feature selection approach using feature association map for supervised and unsupervised classification","volume":"88","author":"Das","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.patrec.2019.12.022","article-title":"An efficient unsupervised feature selection procedure through feature clustering","volume":"131","author":"Yan","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2021.02.034","article-title":"Supervised feature selection using integration of densest subgraph finding with floating forward\u2013backward search","volume":"566","author":"Bhadra","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3847","DOI":"10.3233\/IFS-162156","article-title":"An efficient feature selection technique for clustering based on a new measure of feature importance","volume":"32","author":"Goswami","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kumar, G., Jain, G., Panday, M., Das, A., and Goswami, S. (2020). Graph-based supervised feature selection using correlation exponential. Emerging Technology in Modelling and Graphics, Springer.","DOI":"10.1007\/978-981-13-7403-6_4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.asoc.2020.106421","article-title":"Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data","volume":"93","author":"Peralta","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s10115-019-01341-6","article-title":"Relevant feature selection and ensemble classifier design using bi-objective genetic algorithm","volume":"62","author":"Das","year":"2020","journal-title":"Knowl. Inf. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","article-title":"A review of clustering techniques and developments","volume":"267","author":"Saxena","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_38","first-page":"500","article-title":"A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data","volume":"2","author":"Saxena","year":"2022","journal-title":"Adv. Artif. Intell. Mach. Learn."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/1\/28\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:57:08Z","timestamp":1760119028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/1\/28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,3]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["a16010028"],"URL":"https:\/\/doi.org\/10.3390\/a16010028","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,3]]}}}