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We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.<\/jats:p>","DOI":"10.3233\/jifs-210336","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T14:07:01Z","timestamp":1620742021000},"page":"12243-12252","source":"Crossref","is-referenced-by-count":3,"title":["From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering"],"prefix":"10.1177","volume":"40","author":[{"given":"Kaijie","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanyu","family":"E","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, 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