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UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the connected-component algorithm for finding clusters. Then determines cluster centers by intersecting the narrow-paths. Experimental results confirmed the superiority of our proposed method compared to the two most well-known density-based clustering algorithms, DBSCAN and DENCLUE.<\/jats:p>","DOI":"10.3233\/jifs-16360","type":"journal-article","created":{"date-parts":[[2017,2,28]],"date-time":"2017-02-28T11:38:00Z","timestamp":1488281880000},"page":"2393-2411","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["UALM: Unsupervised Active Learning Method for clustering low-dimensional data"],"prefix":"10.1177","volume":"32","author":[{"given":"Mohammad","family":"Javadian","sequence":"first","affiliation":[{"name":"Energy Department, Kermanshah University of Technology, Kermanshah, Iran"},{"name":"Electrical Engineering Department, Sharif University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed Bagheri","family":"Shouraki","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Sharif University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,2,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-012-0824-6"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.7763\/IJMLC.2013.V3.262"},{"key":"e_1_3_1_4_2","volume-title":"Finding groups in data: An introduction to cluster analysis","author":"Kaufman L.","year":"2009","unstructured":"KaufmanL. and RousseeuwP.J., Finding groups in data: An introduction to cluster analysis, vol. 344: John Wiley & Sons, 2009."},{"key":"e_1_3_1_5_2","article-title":"Fuzziness based i-supervised learning approach for intrusion detection system","author":"Ashfaq R.A.R.","year":"2016","unstructured":"AshfaqR.A.R., WangX.-Z., HuangJ.Z., AbbasH. and HeY.-L., Fuzziness based i-supervised learning approach for intrusion detection system, Information Sciences (2016)\u2013sem.","journal-title":"Information Sciences"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.118"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28349-8_2"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1201\/b15410"},{"key":"e_1_3_1_9_2","author":"Han J.","year":"2006","unstructured":"HanJ., KamberM., PeiJ., Data mining: Concepts and techniques, 2006.","journal-title":"Data mining: Concepts and techniques"},{"key":"e_1_3_1_10_2","unstructured":"TanP.-N. 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