{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:33:21Z","timestamp":1781562801733,"version":"3.54.5"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T00:00:00Z","timestamp":1523404800000},"content-version":"vor","delay-in-days":100,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, <jats:italic>K<\/jats:italic>\u2010means and fuzzy <jats:italic>C<\/jats:italic> means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets\u2019 attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate \u201cinsignificant\u201d irregularities while keeping \u201cmeaningful\u201d singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for <jats:italic>K<\/jats:italic>\u2010means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms\u2019 accuracy rates.<\/jats:p>","DOI":"10.1155\/2018\/9034647","type":"journal-article","created":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T23:35:35Z","timestamp":1523489735000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy <i>C<\/i> Means and <i>K<\/i>\u2010Means Algorithms"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8725-6719","authenticated-orcid":false,"given":"Yeliz","family":"Karaca","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Cattani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majaz","family":"Moonis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4114-4305","authenticated-orcid":false,"given":"\u015eeng\u00fcl","family":"Bayrak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,11]]},"reference":[{"key":"e_1_2_7_1_2","volume-title":"Fractals Everywhere","author":"Michael B.","year":"1988"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/535048"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.012579499"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.115.011771"},{"key":"e_1_2_7_5_2","first-page":"24","article-title":"Atrial fibrillation is associated with anterior predominant white matter lesions in patients presenting with embolic stroke","volume":"47","author":"Mayasi Y.","year":"2017","journal-title":"Journal of Neurology, Neurosurgery & Psychiatry"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2017.04.040"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2004.06.003"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2017-316074.3"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(10)70104-6"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/961039"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1161\/str.48.suppl_1.wp272"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/e18050194"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-2649-7"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218348X17400011"},{"key":"e_1_2_7_15_2","unstructured":"TsanevaG. 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