{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:47:28Z","timestamp":1769770048304,"version":"3.49.0"},"reference-count":17,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms.<\/jats:p>","DOI":"10.3233\/jifs-222912","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:47:39Z","timestamp":1680608859000},"page":"10325-10335","source":"Crossref","is-referenced-by-count":0,"title":["Research and improvement of C-means clustering algorithm based on Image segmentation application"],"prefix":"10.1177","volume":"44","author":[{"given":"Chunying","family":"Wang","sequence":"first","affiliation":[{"name":"School of Water Resources and Environment, China University of Geosciences, Beijing, China"}]},{"given":"Jiahui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Big Data Application Center, Hebei Sailhero Environmental Protection High-Tech Co., Ltd., Shijiazhuang, China"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Water Resources and Environment, China University of Geosciences, Beijing, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-222912_ref1","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.knosys.2018.09.007","article-title":"stratified sampling based clustering algorithm for large-scale data","volume":"163","author":"Zhao","year":"2019","journal-title":"Knowledge-Based Systems"},{"issue":"11","key":"10.3233\/JIFS-222912_ref4","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1007\/s00500-016-2198-7","article-title":"Effective fuzzy possibilistic C-means: an analyzing cancer medical database","volume":"21","author":"Ramathilagan","year":"2017","journal-title":"Soft computing: A Fusion of Foundations, Methodologies and Applications"},{"issue":"1","key":"10.3233\/JIFS-222912_ref5","first-page":"1","article-title":"A hybrid bio-geography based optimization and fuzzy C-means algorithm for image segmentation","volume":"23","author":"Zhang","year":"2017","journal-title":"Soft Computing"},{"issue":"1","key":"10.3233\/JIFS-222912_ref6","first-page":"131","article-title":"MapReduce-based Fuzzy C-means algorithm for distributed document clustering","volume":"103","author":"Sardar","year":"2022","journal-title":"Journal of The Institution of Engineers (India), Series B. Electrical Eingineering, Electronics and Telecommunication Engineering, Computer Engineering"},{"issue":"3","key":"10.3233\/JIFS-222912_ref7","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1049\/ipr2.12064","article-title":"Noise robust intuitionistic fuzzy C-means clustering algorithm incorporating local information","volume":"15","author":"Yang","year":"2021","journal-title":"IET Image Processing"},{"key":"10.3233\/JIFS-222912_ref8","doi-asserted-by":"crossref","first-page":"4500","DOI":"10.1109\/ACCESS.2019.2963444","article-title":"An intuitionistic kernel-based fuzzy C-means clustering algorithm with local information for power equipment image segmentation","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"issue":"9","key":"10.3233\/JIFS-222912_ref9","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1109\/TFUZZ.2020.3003441","article-title":"A new membership scaling fuzzy C-means clustering algorithm","volume":"29","author":"Zhou","year":"2021","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"12","key":"10.3233\/JIFS-222912_ref10","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1049\/iet-ipr.2019.0899","article-title":"MapReduce framework based big data clustering using fractional integrated sparse fuzzy C-means algorithm","volume":"14","author":"Kulkarni","year":"2020","journal-title":"IET Image Processing"},{"issue":"3","key":"10.3233\/JIFS-222912_ref11","doi-asserted-by":"crossref","first-page":"3727","DOI":"10.3233\/JIFS-212863","article-title":"Modified fuzzy C means and K-means clustering based spectrum sensing using cooperative spectrum for cognitive radio networks applications","volume":"43","author":"Velappan","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"2","key":"10.3233\/JIFS-222912_ref12","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.3233\/JIFS-189771","article-title":"Kernel fuzzy C-means clustering with teaching learning based optimization algorithm (TLBO-KFCM)","volume":"42","author":"Singh","year":"2022","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"issue":"1","key":"10.3233\/JIFS-222912_ref13","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/TFUZZ.2020.3022080","article-title":"Acloud-edge-aided incremental high-order possibilistic C-means algorithm for medical data clustering","volume":"29","author":"Bu","year":"2020","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"4","key":"10.3233\/JIFS-222912_ref15","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1515\/jisys-2016-0241","article-title":"A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation","volume":"27","author":"Aruna Kumar","year":"2018","journal-title":"Journal of Intelligent Systems"},{"issue":"1\/2","key":"10.3233\/JIFS-222912_ref16","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10617-017-9200-1","article-title":"Efficient fuzzy C-means based multilevel image segmentation for brain tumor detection in MR images","volume":"22","author":"Shanmuga Priya","year":"2018","journal-title":"Design Automation for Embedded Systems"},{"issue":"Jan.1","key":"10.3233\/JIFS-222912_ref17","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.knosys.2018.09.007","article-title":"A stratified sampling based clustering algorithm for large-scale data","volume":"163","author":"Zhao","year":"2019","journal-title":"Knowledge-Based Systems"},{"issue":"99","key":"10.3233\/JIFS-222912_ref18","first-page":"1","article-title":"Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering","author":"Lei","year":"2018","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"4","key":"10.3233\/JIFS-222912_ref19","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s11036-020-01550-2","article-title":"A de-ann inspired skin cancer detection approach using fuzzy C-means clustering","volume":"25","author":"Kumar","year":"2020","journal-title":"Mobile Networks and Applications"},{"issue":"1","key":"10.3233\/JIFS-222912_ref20","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.gltp.2022.03.028","article-title":"An improved method for text detection using Adam optimization algorithm","volume":"3","author":"Kohli","year":"2022","journal-title":"Global Transitions Proceedings"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-222912","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T08:54:24Z","timestamp":1769676864000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-222912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":17,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-222912","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}