{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:23:18Z","timestamp":1770492198688,"version":"3.49.0"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vietnam J. Comp. Sci."],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:p>Clustering is a key method in unsupervised learning with various applications in data mining, pattern recognition and intelligent information processing. However, the number of groups to be formed, usually notated as [Formula: see text] is a vital parameter for most of the existing clustering algorithms as their clustering results depend heavily on this parameter. The problem of finding the optimal [Formula: see text] value is very challenging. This paper proposes a novel idea for finding the correct number of groups in a dataset based on data depth. The idea is to avoid the traditional process of running the clustering algorithm over a dataset for [Formula: see text] times and further, finding the [Formula: see text] value for a dataset without setting any specific search range for [Formula: see text] parameter. We experiment with different indices, namely CH, KL, Silhouette, Gap, CSP and the proposed method on different real and synthetic datasets to estimate the correct number of groups in a dataset. The experimental results on real and synthetic datasets indicate good performance of the proposed method.<\/jats:p>","DOI":"10.1142\/s2196888820500232","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T17:06:53Z","timestamp":1594314413000},"page":"417-431","source":"Crossref","is-referenced-by-count":5,"title":["A Criterion for Deciding the Number of Clusters in a Dataset Based on Data Depth"],"prefix":"10.1142","volume":"07","author":[{"given":"Ishwar","family":"Baidari","sequence":"first","affiliation":[{"name":"Department of Computer Science, Karnatak University, Dharwad, Karnataka 580003, India"}]},{"given":"Channamma","family":"Patil","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Karnatak University, Dharwad, Karnataka 580003, India"}]}],"member":"219","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"issue":"15","key":"S2196888820500232BIB001","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1016\/j.patrec.2005.04.007","volume":"26","author":"Kim M.","year":"2005","journal-title":"Pattern Recogn. 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