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The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm\u2014by the detection of oneelement clusters\u2014allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.<\/jats:p>","DOI":"10.2478\/v10006-010-0009-3","type":"journal-article","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T01:03:15Z","timestamp":1269997395000},"page":"123-134","source":"Crossref","is-referenced-by-count":27,"title":["A complete gradient clustering algorithm formed with kernel estimators"],"prefix":"10.61822","volume":"20","author":[{"given":"Piotr","family":"Kulczycki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ma\u0142gorzata","family":"Charytanowicz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"37438","reference":[{"key":"1","volume-title":"Cluster Analysis for Applications","author":"M. 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