{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T07:42:06Z","timestamp":1784706126422,"version":"3.55.0"},"reference-count":68,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876101"],"award-info":[{"award-number":["61876101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ipm.2026.104838","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:32:36Z","timestamp":1776875556000},"page":"104838","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PA","title":["FFC: Clustering via identification of edge objects based on feature-force"],"prefix":"10.1016","volume":"63","author":[{"given":"Shengqiang","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4976-9227","authenticated-orcid":false,"given":"Xiyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4015-8033","authenticated-orcid":false,"given":"Jianhua","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ipm.2026.104838_b1","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TCE.2024.3354189","article-title":"Anomaly detection in public street lighting data using unsupervised clustering","volume":"70","author":"Ali","year":"2024","journal-title":"IEEE Transactions on Consumer Electronics"},{"issue":"2","key":"10.1016\/j.ipm.2026.104838_b2","doi-asserted-by":"crossref","DOI":"10.14569\/IJACSA.2024.0150292","article-title":"Enhancing K-means clustering results with gradient boosting: A post-processing approach","volume":"15","author":"Alzakan","year":"2024","journal-title":"International Journal of Advanced Computer Science & Applications"},{"issue":"1","key":"10.1016\/j.ipm.2026.104838_b3","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","author":"Birant","year":"2007","journal-title":"Data & Knowledge Engineering"},{"key":"10.1016\/j.ipm.2026.104838_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113498","article-title":"Anchor graph based connectivity peaks clustering","volume":"319","author":"Cai","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104838_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113498","article-title":"Anchor graph based connectivity peaks clustering","author":"Cai","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104838_b6","doi-asserted-by":"crossref","first-page":"7567","DOI":"10.1109\/TMM.2024.3369862","article-title":"Wasserstein embedding learning for deep clustering: A generative approach","volume":"26","author":"Cai","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.ipm.2026.104838_b7","doi-asserted-by":"crossref","unstructured":"Cakmak, E., Plank, M., Calovi, D. S., Jordan, A., & Keim, D. (2021). Spatio-temporal clustering benchmark for collective animal behavior. In Proceedings of the 1st ACM sIGSPATIAL international workshop on animal movement ecology and human mobility (pp. 5\u20138).","DOI":"10.1145\/3486637.3489487"},{"issue":"6","key":"10.1016\/j.ipm.2026.104838_b8","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","author":"Canny","year":"1986","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2026.104838_b9","series-title":"Deep discriminative clustering analysis","author":"Chang","year":"2019"},{"issue":"8","key":"10.1016\/j.ipm.2026.104838_b10","doi-asserted-by":"crossref","first-page":"11077","DOI":"10.1109\/TNNLS.2023.3248064","article-title":"K-means clustering with natural density peaks for discovering arbitrary-shaped clusters","volume":"35","author":"Cheng","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10.1016\/j.ipm.2026.104838_b11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"Journal of the Royal Statistical Society. Series B. Statistical Methodology"},{"key":"10.1016\/j.ipm.2026.104838_b12","series-title":"2019 20th Asia-Pacific network operations and management symposium","first-page":"1","article-title":"Db-kmeans: an intrusion detection algorithm based on dbscan and k-means","author":"Dong","year":"2019"},{"issue":"4","key":"10.1016\/j.ipm.2026.104838_b13","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/genetics\/154.4.1419","article-title":"The genetical theory of natural selection","volume":"154","author":"Edwards","year":"2000","journal-title":"Genetics"},{"key":"10.1016\/j.ipm.2026.104838_b14","series-title":"Kdd","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"vol. 96","author":"Ester","year":"1996"},{"issue":"458","key":"10.1016\/j.ipm.2026.104838_b15","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1198\/016214502760047131","article-title":"Model-based clustering, discriminant analysis, and density estimation","volume":"97","author":"Fraley","year":"2002","journal-title":"Journal of the American Statistical Association"},{"key":"10.1016\/j.ipm.2026.104838_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127049","article-title":"Few-shot meta spectral clustering with knowledge reuse","volume":"276","author":"Gu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104838_b17","article-title":"Density peaks clustering algorithm via fusing natural neighbor and fuzzy information","author":"Guo","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104838_b18","series-title":"Pattern recognition: 40th german conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, proceedings 40","first-page":"18","article-title":"Associative deep clustering: Training a classification network with no labels","author":"Haeusser","year":"2019"},{"key":"10.1016\/j.ipm.2026.104838_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2026.123372","article-title":"A novel assignment strategy based on fuzzy number cluster centers to assign data points into clusters","author":"Han","year":"2026","journal-title":"Information Sciences"},{"key":"10.1016\/j.ipm.2026.104838_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107327","article-title":"Pixel-level clustering network for unsupervised image segmentation","volume":"127","author":"Hoang","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104838_b21","doi-asserted-by":"crossref","DOI":"10.1109\/TCYB.2025.3537108","article-title":"Automated cluster elimination guided by high-density points","author":"Hu","year":"2025","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"10.1016\/j.ipm.2026.104838_b22","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TETCI.2024.3353598","article-title":"Deepclue: Enhanced deep clustering via multi-layer ensembles in neural networks","volume":"8","author":"Huang","year":"2024","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.1016\/j.ipm.2026.104838_b23","doi-asserted-by":"crossref","unstructured":"Huang, J., Gong, S., & Zhu, X. (2020). Deep semantic clustering by partition confidence maximisation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8849\u20138858).","DOI":"10.1109\/CVPR42600.2020.00887"},{"key":"10.1016\/j.ipm.2026.104838_b24","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"Journal of Classification"},{"key":"10.1016\/j.ipm.2026.104838_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121541","article-title":"A reinforcement learning recommender system using bi-clustering and Markov decision process","volume":"237","author":"Iftikhar","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104838_b26","doi-asserted-by":"crossref","unstructured":"Ji, X., Henriques, J. F., & Vedaldi, A. (2019). Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9865\u20139874).","DOI":"10.1109\/ICCV.2019.00996"},{"key":"10.1016\/j.ipm.2026.104838_b27","article-title":"FDGC: Fuzzy deep clustering with dual-granularity contrastive learning","volume":"310","author":"Ju","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104838_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102699","article-title":"D3WC: Deep three-way clustering with granular evidence fusion","volume":"114","author":"Ju","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104838_b29","series-title":"Finding groups in data: an introduction to cluster analysis","author":"Kaufman","year":"2009"},{"key":"10.1016\/j.ipm.2026.104838_b30","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, P., Liu, Z., Peng, D., Zhou, J. T., & Peng, X. (2021). Contrastive clustering. vol. 35, In Proceedings of the AAAI conference on artificial intelligence (pp. 8547\u20138555). 10.","DOI":"10.1609\/aaai.v35i10.17037"},{"issue":"3","key":"10.1016\/j.ipm.2026.104838_b31","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1108\/PMM-05-2021-0026","article-title":"A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering","volume":"22","author":"Lund","year":"2021","journal-title":"Performance Measurement and Metrics"},{"key":"10.1016\/j.ipm.2026.104838_b32","unstructured":"MacQueen, J., et al. (1967). Some methods for classification and analysis of multivariate observations. vol. 1, In Proceedings of the fifth berkeley symposium on mathematical statistics and probability (pp. 281\u2013297). Oakland, CA, USA: 14."},{"issue":"11","key":"10.1016\/j.ipm.2026.104838_b33","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: a lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.ipm.2026.104838_b34","series-title":"Introduction to pattern recognition and machine learning","author":"Murty","year":"2015"},{"key":"10.1016\/j.ipm.2026.104838_b35","series-title":"Philosophiae naturalis principia mathematica","author":"Newton","year":"1979"},{"key":"10.1016\/j.ipm.2026.104838_b36","article-title":"On spectral clustering: Analysis and an algorithm","volume":"14","author":"Ng","year":"2001","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"5","key":"10.1016\/j.ipm.2026.104838_b37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3643564","article-title":"A survey on automl methods and systems for clustering","volume":"18","author":"Poulakis","year":"2024","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.ipm.2026.104838_b38","doi-asserted-by":"crossref","unstructured":"Qian, Q. (2023). Stable cluster discrimination for deep clustering. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 16645\u201316654).","DOI":"10.1109\/ICCV51070.2023.01526"},{"key":"10.1016\/j.ipm.2026.104838_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129359","article-title":"Deep clustering of tabular data by weighted Gaussian distribution learning","volume":"623","author":"Rabbani","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104838_b40","series-title":"International conference on machine learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"issue":"6","key":"10.1016\/j.ipm.2026.104838_b41","article-title":"Advanced quantum algorithms for big data clustering and high-dimensional classification","volume":"4","author":"Ram\u00edrez","year":"2024","journal-title":"Journal of Advanced Computing Systems"},{"issue":"8","key":"10.1016\/j.ipm.2026.104838_b42","doi-asserted-by":"crossref","first-page":"8219","DOI":"10.1007\/s10462-022-10366-3","article-title":"Comprehensive survey on hierarchical clustering algorithms and the recent developments","volume":"56","author":"Ran","year":"2023","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"10.1016\/j.ipm.2026.104838_b43","first-page":"73","article-title":"A critical review on density-based clustering algorithms and their performance in data mining","volume":"9","author":"Ravi","year":"2022","journal-title":"International Journal of Research and Analytical Reviews(IJRAR)"},{"issue":"4","key":"10.1016\/j.ipm.2026.104838_b44","doi-asserted-by":"crossref","first-page":"5858","DOI":"10.1109\/TNNLS.2024.3403155","article-title":"Deep clustering: A comprehensive survey","volume":"36","author":"Ren","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6191","key":"10.1016\/j.ipm.2026.104838_b45","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"issue":"5","key":"10.1016\/j.ipm.2026.104838_b46","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1109\/TPAMI.2023.3337195","article-title":"A flexible EM-like clustering algorithm for noisy data","volume":"46","author":"Roizman","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2026.104838_b47","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":"10.1016\/j.ipm.2026.104838_b48","doi-asserted-by":"crossref","unstructured":"Shen, S., Li, W., Wang, X., Zhang, D., Jin, Z., Zhou, J., & Lu, J. (2023). Clip-cluster: Clip-guided attribute hallucination for face clustering. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 20786\u201320795).","DOI":"10.1109\/ICCV51070.2023.01900"},{"issue":"1","key":"10.1016\/j.ipm.2026.104838_b49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1348\/000711005X48266","article-title":"K-means clustering: a half-century synthesis","volume":"59","author":"Steinley","year":"2006","journal-title":"British Journal of Mathematical and Statistical Psychology"},{"issue":"Dec","key":"10.1016\/j.ipm.2026.104838_b50","first-page":"583","article-title":"Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"Journal of Machine Learning Research"},{"issue":"7","key":"10.1016\/j.ipm.2026.104838_b51","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10489-025-06295-7","article-title":"Deep attribute graph clustering based on bisymmetric network information fusion and mutual influence","volume":"55","author":"Tan","year":"2025","journal-title":"Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies"},{"key":"10.1016\/j.ipm.2026.104838_b52","article-title":"Clustering interval and triangular granular data: Modeling, execution, and assessment","author":"Tang","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ipm.2026.104838_b53","series-title":"Clustering-friendly representation learning via instance discrimination and feature decorrelation","author":"Tao","year":"2021"},{"issue":"Preprint","key":"10.1016\/j.ipm.2026.104838_b54","first-page":"1","article-title":"Cluster analysis via projection onto convex sets","author":"Tran","year":"2024","journal-title":"Intelligent Data Analysis"},{"key":"10.1016\/j.ipm.2026.104838_b55","unstructured":"Tsai, T. W., Li, C., & Zhu, J. (2020). Mice: Mixture of contrastive experts for unsupervised image clustering. In International conference on learning representations."},{"issue":"127","key":"10.1016\/j.ipm.2026.104838_b56","first-page":"1","article-title":"Graph clustering with graph neural networks","volume":"24","author":"Tsitsulin","year":"2023","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.ipm.2026.104838_b57","series-title":"European conference on computer vision","first-page":"268","article-title":"Scan: Learning to classify images without labels","author":"Van Gansbeke","year":"2020"},{"key":"10.1016\/j.ipm.2026.104838_b58","article-title":"Information theoretic measures for clusterings comparison: Variants","volume":"18","author":"Vinh","year":"2009","journal-title":"Properties, Normalization and Correction for Chance"},{"key":"10.1016\/j.ipm.2026.104838_b59","article-title":"Neural connected kernel based multiple kernel clustering","author":"Wang","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104838_b60","series-title":"2024 international joint conference on neural networks","first-page":"1","article-title":"Few shot contrastive spectral clustering with meta learning and neighbor mining","author":"Wang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104838_b61","doi-asserted-by":"crossref","unstructured":"Wu, J., Long, K., Wang, F., Qian, C., Li, C., Lin, Z., & Zha, H. (2019). Deep comprehensive correlation mining for image clustering. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 8150\u20138159).","DOI":"10.1109\/ICCV.2019.00824"},{"key":"10.1016\/j.ipm.2026.104838_b62","series-title":"International conference on machine learning","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","author":"Xie","year":"2016"},{"issue":"3","key":"10.1016\/j.ipm.2026.104838_b63","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TETCI.2024.3367811","article-title":"Enhanced adjacency-constrained hierarchical clustering using fine-grained pseudo labels","volume":"8","author":"Yang","year":"2024","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.1016\/j.ipm.2026.104838_b64","doi-asserted-by":"crossref","unstructured":"Yang, J., Parikh, D., & Batra, D. (2016). Joint unsupervised learning of deep representations and image clusters. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5147\u20135156).","DOI":"10.1109\/CVPR.2016.556"},{"key":"10.1016\/j.ipm.2026.104838_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122270","article-title":"A feature-weighted suppressed possibilistic fuzzy c-means clustering algorithm and its application on color image segmentation","volume":"241","author":"Yu","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104838_b66","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Sun, Y., & Mao, W. (2021). MCCN: Multimodal coordinated clustering network for large-scale cross-modal retrieval. In Proceedings of the 29th ACM international conference on multimedia (pp. 5427\u20135435).","DOI":"10.1145\/3474085.3475670"},{"issue":"1s","key":"10.1016\/j.ipm.2026.104838_b67","first-page":"1","article-title":"Gaussian mixture model clustering with incomplete data","volume":"17","author":"Zhang","year":"2021","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"issue":"2","key":"10.1016\/j.ipm.2026.104838_b68","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: an efficient data clustering method for very large databases","volume":"25","author":"Zhang","year":"1996","journal-title":"ACM Sigmod Record"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326002293?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326002293?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:13:28Z","timestamp":1783178008000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326002293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["S0306457326002293"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104838","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FFC: Clustering via identification of edge objects based on feature-force","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104838","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104838"}}