{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:29:51Z","timestamp":1763101791966,"version":"3.45.0"},"reference-count":55,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":74,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.057693","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T03:33:27Z","timestamp":1736393607000},"page":"4949-4976","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["<i>k<\/i>ProtoClust: Towards Adaptive <i>k<\/i>-Prototype Clustering without Known <i>k<\/i>"],"prefix":"10.32604","volume":"82","author":[{"given":"Yuan","family":"Ping","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huina","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","series-title":"2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)","first-page":"135","article-title":"Customer segmentation using k-means clustering","author":"Kansal","year":"2018"},{"key":"ref2","first-page":"87","volume":"44","author":"Xia","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref3","unstructured":"Banerji A. k-mean: getting the optimal number of clusters; 2022 [cited 2024 Oct 20]. Available from: https:\/\/www.analyticsvidhya.com\/blog\/2021\/05\/k-mean-getting-the-optimal-number-of-clusters\/."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"25070","DOI":"10.3934\/math.20241222","article-title":"Addressing limitations of the k-means clustering algorithm: outliers, non-spherical data, and optimal cluster selection","volume":"9","author":"Khan","year":"2024","journal-title":"AIMS Math"},{"key":"ref5","series-title":"Proceedings of the 16th International Conference on Pattern Recognition (ICPR\u201902)","first-page":"276","article-title":"Data clustering using evidence accumulation","author":"Fred","year":"2002"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.eswa.2012.07.021","article-title":"A comparative study of efficient initialization methods for the k-means clustering algorithm","volume":"40","author":"Celebi","year":"2013","journal-title":"Expert Syst Appl"},{"key":"ref7","series-title":"Proceedings of The Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA \u201907)","first-page":"1027","author":"Arthur","year":"2007"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.patcog.2019.04.014","volume":"93","author":"Fr\u00e4nti","year":"2019","journal-title":"Pattern Recognit"},{"key":"ref9","first-page":"1618","volume":"34","author":"Cap\u00f3","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1109\/TCYB.2019.2916196","volume":"51","author":"Lu","year":"2021","journal-title":"IEEE Trans Cybern"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"4983","DOI":"10.1109\/TNNLS.2020.3026532","volume":"32","author":"Yao","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"146964","DOI":"10.1109\/ACCESS.2019.2945926","article-title":"Efficient training support vector clustering with appropriate boundary information","volume":"7","author":"Ping","year":"2019","journal-title":"IEEE Access"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/TKDE.2018.2842191","article-title":"A rapid hybrid clustering algorithm for large volumes of high dimensional data","volume":"31","author":"Rathore","year":"2019","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"163","DOI":"10.14778\/3425879.3425887","volume":"14","author":"Wang","year":"2020","journal-title":"Proceedings VLDB Endowment"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","article-title":"Support vector domain description","volume":"20","author":"Tax","year":"1999","journal-title":"Pattern Recognit Lett"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/TPAMI.2010.188","article-title":"Selecting critical patterns based on local geometrical and statistical information","volume":"33","author":"Li","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1007\/s11390-012-1232-1","article-title":"Convex decomposition based cluster labeling method for support vector clustering","volume":"27","author":"Ping","year":"2012","journal-title":"J Comput Sci Technol"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1854","DOI":"10.3390\/electronics11121854","article-title":"Improved boundary support vector clustering with self-adaption support","volume":"11","author":"Li","year":"2022","journal-title":"Electronics"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/2.781637","article-title":"CHAMELEON: a hierarchical clustering algorithm using dynamic modeling","volume":"32","author":"Karypis","year":"1999","journal-title":"Computer"},{"key":"ref20","first-page":"265135","article-title":"Fuzzy clustering using the convex hull as geometrical model","volume":"2015","author":"Liparulo","year":"2015","journal-title":"Adv Fuzzy Syst"},{"key":"ref21","unstructured":"Pan C, Rana JSSPV, Milenkovic O. Machine unlearning of federated clusters. arXiv:2210.16424. 2022."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1090\/conm\/026\/737400","article-title":"Extensions of lipschitz mappings into a hilbert space","volume":"26","author":"Johnson","year":"1984","journal-title":"Contemp Math"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.patrec.2020.07.028","volume":"138","author":"Fard","year":"2020","journal-title":"Pattern Recognit Lett"},{"key":"ref24","first-page":"2371","volume":"44","author":"Nie","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref25","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3120","author":"Li","year":"2021"},{"key":"ref26","series-title":"Proceedings of the 30th Annual ACM Symposium on Theory of Computing (STOC\u2019 98)","first-page":"106","author":"Arora","year":"1998"},{"journal-title":"Clustering by means of meoids","year":"1987","author":"Kaufman","key":"ref27"},{"journal-title":"UCI machine learning repository","year":"2010 [cited 2024 Oct 20]","author":"Frank","key":"ref28"},{"key":"ref29","series-title":"Proceedings of 15th Nat\u2019l Conference for Artificial Intelligence (AAAI\u201998)","first-page":"509","article-title":"Learning to extract symbolic knowledge form the world wide web","author":"Graven","year":"1998"},{"key":"ref30","series-title":"Proceedings of 17th Annual ACM SIGIR Conference","first-page":"192","article-title":"Ohsumed: an interactive retrieval evaluation and new large test collection for research","author":"Hersh","year":"1994"},{"key":"ref31","first-page":"73","article-title":"Early tcp traffic classification","volume":"29","author":"Peng","year":"2011","journal-title":"J Appl Sci"},{"journal-title":"KDD cup 99 intrusion detection dataset","year":"1999 [cited 2024 Oct 20]","key":"ref32"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1002\/9780470382776","author":"Xu","year":"2008","journal-title":"Clustering"},{"key":"ref34","first-page":"583","article-title":"Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"J Mach Learn Res"},{"key":"ref35","unstructured":"Sasaki Y. The truth of the f-measure; 2007 [cited 2024 Oct 20]. Available from: https:\/\/www.toyota-ti.ac.jp\/Lab\/Denshi\/COIN\/people\/yutaka.sasaki\/F-measure-YS-26Oct07.pdf."},{"key":"ref36","first-page":"1001","article-title":"A novel scheme for accelerating support vector clustering","volume":"31","author":"Ping","year":"2012","journal-title":"Comput Inform"},{"key":"ref37","first-page":"2677","article-title":"An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons","volume":"9","author":"Garcia","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref38","first-page":"100","article-title":"Improvements of general multiple test procedures for redundant systems of hypotheses","volume":"70","author":"Bergmann","year":"1988","journal-title":"Mult Hypotheses Test"},{"key":"ref39","series-title":"Proceedings of the 13th Annual Conference on Neural Information ProcessingSystems (NIPS\u201916)","first-page":"1352","author":"Newling","year":"2016"},{"key":"ref40","series-title":"Proceedings of the 33rd International Conference on Machine Learning (ICML \u201916)","first-page":"936","author":"Newling","year":"2016"},{"key":"ref41","series-title":"Proceedings of 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC \u201917)","first-page":"94","author":"Kwedlo","year":"2017"},{"key":"ref42","series-title":"Proceedings of the 2016 SIAM International Conference on Data Mining (SDM \u201916)","first-page":"324","author":"Ry\u0161av\u00fd","year":"2016"},{"key":"ref43","series-title":"Proceedings of the 33rd International Conference on Machine Learning (ICML \u201916)","first-page":"2578","author":"Bottesch","year":"2016"},{"key":"ref44","series-title":"Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM \u201914)","first-page":"233","author":"Broder","year":"2014"},{"key":"ref45","first-page":"1295:1\u201312","volume":"9","author":"Ahmed","year":"2020","journal-title":"Electronics"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","article-title":"K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data","volume":"622","author":"Ikotun","year":"2023","journal-title":"Inf Sci"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"11761","DOI":"10.1109\/ACCESS.2024.3350791","article-title":"Inertia-based indices to determine the number of clusters in k-means: an experimental evaluation","volume":"12","author":"Rykov","year":"2024","journal-title":"IEEE Access"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/S0167-8655(99)00069-0","article-title":"An empirical comparison of four initialization methods for the k-means algorithm","volume":"20","author":"Pe\u00f1a","year":"1999","journal-title":"Pattern Recognit Lett"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TCC.2017.2656895","volume":"7","author":"Yuan","year":"2019","journal-title":"IEEE Trans Cloud Comput"},{"key":"ref50","first-page":"3097","volume":"66","author":"Yang","year":"2018","journal-title":"IEEE Trans Antennas Propag"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/JIOT.2017.2677578","volume":"4","author":"Wu","year":"2017","journal-title":"IEEE Internet Things J"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1109\/TKDE.2020.2995748","volume":"34","author":"Nie","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"100504","DOI":"10.1016\/j.eij.2024.100504","article-title":"Determining the optimal number of clusters by enhanced gap statistic in k-mean algorithm","volume":"27","author":"Khan","year":"2024","journal-title":"Egypt Inform J"},{"key":"ref54","first-page":"1","article-title":"Xai beyond classification: interpretable neural clustering","volume":"23","author":"Peng","year":"2022","journal-title":"J Mach Learn Res"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"3994","DOI":"10.1109\/TNNLS.2017.2737941","article-title":"Supprt vector data descriptions and k-means clustering: one class?","volume":"29","author":"Gornitz","year":"2018","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-3\/TSP_CMC_57693\/TSP_CMC_57693.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:28:28Z","timestamp":1763101708000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n3\/59875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":55,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.057693","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-08-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-06","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}