{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T08:59:48Z","timestamp":1782982788392,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Program of the Chongqing Municipal Education Commission, China","award":["KJQN202401911"],"award-info":[{"award-number":["KJQN202401911"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Streaming data is becoming more and more common in the field of big data and incremental frameworks can address its complexity. The BDFCOM algorithm achieves good results on common form datasets by introducing the ordering mechanism of beta distribution weighting. In this paper, based on the BDFCOM algorithm, two incremental beta distribution weighted fuzzy C-ordered means clustering algorithms, SPBDFCOM and OBDFCOM, are proposed by combining the two incremental frameworks of Single-Pass and Online, respectively. In order to validate the performance of SPBDFCOM and OBDFCOM, this paper selects seven real datasets for experiments and compares their performance with six other incremental clustering algorithms using six evaluation metrics. The results show that the two proposed incremental algorithms perform significantly better compared to other algorithms.<\/jats:p>","DOI":"10.3390\/info16080663","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T10:50:21Z","timestamp":1754391021000},"page":"663","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Incremental Beta Distribution Weighted Fuzzy C-Ordered Means Clustering"],"prefix":"10.3390","volume":"16","author":[{"given":"Hengda","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computing, Universiti Utara Malaysia, 06010 Sintok, Malaysia"},{"name":"Artificial Intelligence Department, Chongqing Institute of Engineering, 400056 Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad Farhan","family":"Mohamad Mohsin","sequence":"additional","affiliation":[{"name":"School of Computing, Universiti Utara Malaysia, 06010 Sintok, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Syafiq","family":"Mohd Pozi","sequence":"additional","affiliation":[{"name":"School of Computing, Universiti Utara Malaysia, 06010 Sintok, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Quantitative Sciences, Universiti Utara Malaysia, 06010 Sintok, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1093\/micmic\/ozad067.185","article-title":"Imaging of Chemical Structure from Low-signal-to-noise EELS Enabled by Diffusion Mapping","volume":"29","author":"Colletta","year":"2023","journal-title":"Microsc. Microanal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104743","DOI":"10.1016\/j.engappai.2022.104743","article-title":"A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects","volume":"110","author":"Ezugwu","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5213","DOI":"10.1007\/s11831-022-09779-8","article-title":"A systematic review on generalized fuzzy numbers and its applications: Past, present and future","volume":"29","author":"Kumar","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cardone, B., Di Martino, F., and Senatore, S. (2023). Emotion-based classification through fuzzy entropy-enhanced FCM clustering. Statistical Modeling in Machine Learning, Academic Press.","DOI":"10.1016\/B978-0-323-91776-6.00010-5"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Salve, V.P., and Ghatule, M.P. (2025, January 20-22). Comprehensive Analysis of Clustering Methods: Focusing on Fuzzy Clustering. Proceedings of the 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), Erode, India.","DOI":"10.1109\/ICMSCI62561.2025.10894658"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"122270","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 Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.fss.2014.12.007","article-title":"Fuzzy c-ordered-means clustering","volume":"286","author":"Leski","year":"2016","journal-title":"Fuzzy Sets Syst."},{"key":"ref_8","first-page":"523","article-title":"Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering","volume":"23","author":"Wang","year":"2024","journal-title":"J. Inf. Commun. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rakhonde, G.Y., Ahale, S., Reddy, N.K., Purushotham, P., and Deshkar, A. (2024). Big data analytics for improved weather forecasting and disaster management. Artificial Intelligence and Smart Agriculture: Technology and Applications, Springer Nature.","DOI":"10.1007\/978-981-97-0341-8_9"},{"key":"ref_10","unstructured":"Varshney, A.K., and Torra, V. (2022). Literature Review of various Fuzzy Rule based Systems. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, T., Bi, S., and Xiao, J. (2024, January 13\u201315). Transformer-based financial fraud detection with cloud-optimized real-time streaming. Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management, Zhengzhou, China.","DOI":"10.1145\/3724154.3724271"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, P., Abouelenien, M., Mihalcea, R., Ding, Z., Yang, Q., and Zhou, Y. (June, January 31). Deception detection from linguistic and physiological data streams using bimodal convolutional neural networks. Proceedings of the 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), Guangzhou, China.","DOI":"10.1109\/ISPDS62779.2024.10667569"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26929","DOI":"10.1109\/JIOT.2024.3384493","article-title":"A heterogeneous streaming vehicle data access model for diverse IoT sensor monitoring network management","volume":"11","author":"Zhou","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e1405","DOI":"10.1002\/widm.1405","article-title":"Data stream analysis: Foundations, major tasks and tools","volume":"11","author":"Bahri","year":"2021","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s13222-022-00417-y","article-title":"Algorithms for windowed aggregations and joins on distributed stream processing systems","volume":"22","author":"Verwiebe","year":"2022","journal-title":"Datenbank-Spektrum"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1007\/s10994-023-06353-6","article-title":"A survey on learning from imbalanced data streams: Taxonomy, challenges, empirical study, and reproducible experimental framework","volume":"113","author":"Aguiar","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6439","DOI":"10.1007\/s10462-022-10325-y","article-title":"Data clustering: Application and trends","volume":"56","author":"Oyewole","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","article-title":"Data stream clustering: A review","volume":"54","author":"Atalay","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hore, P., Hall, L.O., and Goldgof, D.B. (2007, January 23\u201326). Single pass fuzzy c means. Proceedings of the 2007 IEEE International Fuzzy Systems Conference, London, UK.","DOI":"10.1109\/FUZZY.2007.4295372"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hore, P., Hall, L., Goldgof, D., and Cheng, W. (2008, January 19\u201322). Online fuzzy c means. Proceedings of the NAFIPS 2008\u20142008 Annual Meeting of the North American Fuzzy Information Processing Society, New York, NY, USA.","DOI":"10.1109\/NAFIPS.2008.4531233"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1198\/jasa.2008.s239","article-title":"Robust Statistics: Theory and methods","volume":"103","author":"Tyler","year":"2008","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_22","unstructured":"Aishwarya, W.A. (2025, July 30). Shill Bidding Dataset (SBD). Available online: https:\/\/www.kaggle.com\/datasets\/aishu2218\/shill-bidding-dataset."},{"key":"ref_23","unstructured":"Mahmoud, L. (2025, July 30). Chronic Kidney Disease Dataset. Available online: https:\/\/www.kaggle.com\/code\/mahmoudlimam\/chronic-kidney-disease-clustering-and-prediction."},{"key":"ref_24","unstructured":"Awan, M. (2025, July 30). Manufacturing Defects Simulation Dataset. Available online: https:\/\/www.kaggle.com\/code\/ksmooi\/manufacturing-defect-prediction-stacking."},{"key":"ref_25","unstructured":"Dua, D., and Graff, C. (2017). UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3606367","article-title":"A review of the F-measure: Its history, properties, criticism, and alternatives","volume":"56","author":"Christen","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.patrec.2006.11.010","article-title":"A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment","volume":"28","author":"Campello","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s12065-020-00544-z","article-title":"Clustering method and sine cosine algorithm for image segmentation","volume":"15","author":"Khrissi","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_29","first-page":"29","article-title":"Incremental Fuzzy C-Ordered Means Clustering","volume":"41","author":"Liu","year":"2018","journal-title":"J. Beijing Univ. Posts Telecommun."},{"key":"ref_30","first-page":"8566253","article-title":"Incremental fuzzy clustering based on feature reduction","volume":"2022","author":"Liu","year":"2022","journal-title":"J. Electr. Comput. Eng."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/8\/663\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:22:20Z","timestamp":1760034140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/8\/663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":30,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["info16080663"],"URL":"https:\/\/doi.org\/10.3390\/info16080663","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,3]]}}}