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In this paper, we first introduce a unified view\nof density-based clustering algorithms. We then build upon this view and bridge the\nareas of semi-supervised clustering and classification under a common umbrella of\ndensity-based techniques. We show that there are close relations between\ndensity-based clustering algorithms and the graph-based approach for transductive\nclassification. These relations are then used as a basis for a new framework for\nsemi-supervised classification based on building-blocks from density-based\nclustering. This framework is not only efficient and effective, but it is also\nstatistically sound. In addition, we generalize the core algorithm in our framework,\nHDBSCAN*, so that it can also perform semi-supervised clustering by directly taking\nadvantage of any fraction of labeled data that may be available. Experimental\nresults on a large collection of datasets show the advantages of the proposed\napproach both for semi-supervised classification as well as for semi-supervised\nclustering.<\/jats:p>","DOI":"10.1007\/s10618-019-00651-1","type":"journal-article","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T12:02:31Z","timestamp":1566561751000},"page":"1894-1952","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A unified view of density-based methods for\nsemi-supervised clustering and classification"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0861-6681","authenticated-orcid":false,"given":"Jadson","family":"Castro Gertrudes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arthur","family":"Zimek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Sander","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo J. 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