{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:47:52Z","timestamp":1791928072762,"version":"4.3.4"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019587","name":"Islamic University of Indonesia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019587","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["MethodsX"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.mex.2026.104198","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:21:56Z","timestamp":1791501716000},"page":"104198","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A variance-guided resampling framework for better K-medoids clustering"],"prefix":"10.1016","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5270-1309","authenticated-orcid":false,"family":"Kariyam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9500-2268","authenticated-orcid":false,"given":"Feri","family":"Wijayanto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edy","family":"Widodo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.mex.2026.104198_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2021.101804","article-title":"Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms","volume":"101","author":"Schubert","year":"2021","journal-title":"Inf. Syst."},{"key":"10.1016\/j.mex.2026.104198_bib0002","series-title":"Clustering","author":"Xu","year":"2009"},{"key":"10.1016\/j.mex.2026.104198_bib0003","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s00357-007-0003-0","article-title":"Initializing k-means batch clustering: a critical evaluation of several techniques","volume":"24","author":"Douglas S","year":"2007","journal-title":"J. Classif."},{"key":"10.1016\/j.mex.2026.104198_bib0004","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.052","article-title":"An improved k-medoids algorithm based on step increasing and optimizing medoids","volume":"92","author":"Dunghou","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.mex.2026.104198_bib0005","doi-asserted-by":"crossref","unstructured":"H. Song, J.G. Lee, W.S. Han, PAMAE: Parallel k-medoids clustering with high accuracy and efficiency, Research Paper, Halifax NS. Canada, (2017). 10.1145\/3097983.3098098.","DOI":"10.1145\/3097983.3098098"},{"issue":"2","key":"10.1016\/j.mex.2026.104198_bib0006","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","article-title":"A simple and fast algorithm for k-medoids clustering","volume":"36","author":"Park","year":"2009","journal-title":"Expert Syst. Appl."},{"issue":"177","key":"10.1016\/j.mex.2026.104198_bib0007","first-page":"1","article-title":"Simple k-medoids partitioning algorithm for mixed variable data","volume":"12","author":"Budiaji","year":"2019","journal-title":"Algorithms"},{"issue":"6","key":"10.1016\/j.mex.2026.104198_bib0008","first-page":"1613","article-title":"Block-based k-medoids partitioning method with standardized data to improve clustering accuracy","volume":"9","author":"Kariyam","year":"2022","journal-title":"Math. Model. Eng. Probl."},{"key":"10.1016\/j.mex.2026.104198_bib0009","series-title":"Clustering of Datasets Using The Object Block-Based K-Medoids Partitioning Method","author":"Kariyam","year":"2024"},{"key":"10.1016\/j.mex.2026.104198_bib0010","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1007\/s00357-019-09328-2","article-title":"Effect of resampling in determining the number of clusters in a data set","volume":"37","author":"Dangl","year":"2020","journal-title":"J. Classif."},{"issue":"1","key":"10.1016\/j.mex.2026.104198_bib0011","first-page":"265","article-title":"Comparative analysis of distance functions on DBSCAN algorithm: mapping malnourished toddlers in Medan City, Indonesia","volume":"6","author":"Karo","year":"2025","journal-title":"J. Soft Comput. Data Min."},{"issue":"1A","key":"10.1016\/j.mex.2026.104198_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15392\/bjrs.v9i1A.1324","article-title":"The effect of data standardization in cluster analysis","volume":"9","author":"Nogueira","year":"2021","journal-title":"Braz. J. Radiat. Sci."},{"key":"10.1016\/j.mex.2026.104198_bib0013","article-title":"An effective clustering scheme for high-dimensional data","author":"Xuansen","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.mex.2026.104198_bib0014","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.neucom.2018.12.093","article-title":"An intelligent clustering algorithm for high-dimensional Multiview data in big data applications","volume":"393","author":"Tao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.mex.2026.104198_bib0015","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s41060-020-00216-2","article-title":"Clustering of mixed-type data considering concept hierarchies: problem specification and algorithm","volume":"10","author":"Behzadi","year":"2020","journal-title":"Int. J. Data Sci. Anal."},{"issue":"1","key":"10.1016\/j.mex.2026.104198_bib0016","first-page":"127","article-title":"A fuzzy-based cluster head selection technique for optimizing communication of VANETs","volume":"6","author":"Yousif","year":"2025","journal-title":"J. Soft Comput. Data Min."},{"issue":"1","key":"10.1016\/j.mex.2026.104198_bib0017","first-page":"18","article-title":"Forsyth-Edwards notation in chess game clustering: a depth-based evaluation","volume":"4","author":"Wijayanto","year":"2025","journal-title":"J. Sains Nalar dan Apl. Teknol. Inf."},{"issue":"2","key":"10.1016\/j.mex.2026.104198_bib0018","first-page":"334","article-title":"Spatial mapping of pavement distress using digital image data and a CNN-assisted clustering framework","volume":"3","author":"Kariyam","year":"2026","journal-title":"Math. Model. Eng. Probl."},{"key":"10.1016\/j.mex.2026.104198_bib0019","series-title":"Proceedings of the AIP Conference Proceedings (Indexed Scopus Proceeding)","article-title":"Comparison of several clustering methods in classifying countries based on the environmental performance index","volume":"3248","author":"Kariyam","year":"2025"},{"issue":"216","key":"10.1016\/j.mex.2026.104198_bib0020","article-title":"Classifying insurance reserve period via claim frequency domain using hawkes process","volume":"10","author":"Effendie","year":"2022","journal-title":"Risks"},{"key":"10.1016\/j.mex.2026.104198_bib0021","doi-asserted-by":"crossref","first-page":"23","DOI":"10.2307\/2531893","article-title":"A criterion for determining the number of groups in a data set using sum-of-squares clustering","volume":"44","author":"Krzanowski","year":"1988","journal-title":"J. Bimetric"},{"issue":"2","key":"10.1016\/j.mex.2026.104198_bib0022","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/1467-9868.00293","article-title":"Estimating the number of clusters in a data set via the gap statistic","volume":"63","author":"Tibshirani","year":"2001","journal-title":"J. R. Stat. Soc. B (Stat. Methodol.)"},{"issue":"1","key":"10.1016\/j.mex.2026.104198_bib0023","first-page":"1","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Calinski","year":"1974","journal-title":"Commun. Stat. Theory Methods"},{"key":"10.1016\/j.mex.2026.104198_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2023.102084","article-title":"A medoid-based deviation ratio index to determine the number of clusters in a datasets","volume":"10","author":"Kariyam","year":"2023","journal-title":"MethodsX"},{"key":"10.1016\/j.mex.2026.104198_bib0025","unstructured":"W. Wolberg, O. Mangasarian, N. Street, W. Street, Breast cancer Wisconsin (diagnostic) dataset, UCI machine learning repository, (1993). DOI: 10.24432\/C5DW2B."},{"key":"10.1016\/j.mex.2026.104198_bib0026","unstructured":"M. Zwitter, M. Soklic, Primary Tumor Dataset. UCI Machine Learning Repository, (1998). DOI: 10.24432\/C5WK5Q."},{"key":"10.1016\/j.mex.2026.104198_bib0027","unstructured":"A. Janosi, W. Steinbrunn, M. Pfisterer, R. Detrano, Heart Disease Dataset, UCI Machine Learning Repository, (1988). DOI: 10.24432\/C52P4X."},{"key":"10.1016\/j.mex.2026.104198_bib0028","unstructured":"R.A. Fisher, Iris Dataset, UCI Machine Learning Repository, (1983). DOI: 10.24432\/C56C76."},{"key":"10.1016\/j.mex.2026.104198_bib0029","unstructured":"S. Aeberhard, M. Forina, Wine Dataset, UCI Machine Learning Repository, (1991). DOI: 10.24432\/C5PC7J."},{"key":"10.1016\/j.mex.2026.104198_bib0030","unstructured":"R. Michalski, Soybean (Small) Dataset, UCI Machine Learning Repository, (1987). DOI: 10.24432\/C5DS3P."}],"container-title":["MethodsX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016126004140?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016126004140?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:25:26Z","timestamp":1791926726000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2215016126004140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":30,"alternative-id":["S2215016126004140"],"URL":"https:\/\/doi.org\/10.1016\/j.mex.2026.104198","relation":{},"ISSN":["2215-0161"],"issn-type":[{"value":"2215-0161","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A variance-guided resampling framework for better K-medoids clustering","name":"articletitle","label":"Article Title"},{"value":"MethodsX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mex.2026.104198","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104198"}}