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In this study, a heuristic calculation, deer hunting optimization algorithm (DHOA), was adjusted for K-means data clustering by altering the fundamental parameters of DHOA calculation, which are propelled from the characteristic enlivened calculations. During this work, a new human-based descriptive DHOA has been developed following a human deer hunting strategy. In order to attack the fawn, hunters update their positions based on the movement of the leader and backward movement while also considering the angle of the deer. In this work, the DHOA was hybridized with K-means clustering and the performance of the proposed approach is tested against UCI repository data with different algorithms. <\/jats:p>","DOI":"10.1142\/s1793962323410155","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T14:14:25Z","timestamp":1650636865000},"source":"Crossref","is-referenced-by-count":1,"title":["Deer hunting optimization technique for clustering unsupervised data in data mining"],"prefix":"10.1142","volume":"14","author":[{"given":"Hayder Hussein","family":"Azeez","sequence":"first","affiliation":[{"name":"Southern Technical University, Iraq"}]}],"member":"219","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"issue":"3","key":"S1793962323410155BIB001","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"Jain K.","year":"1999","journal-title":"ACM Comput. 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