Data objects with mixed numerical and categorical attributes are often dealt with in the real world. Most existing algorithms have limitations such as low clustering quality, cluster center determination difficulty, and initial parameter sensibility. A fast density clustering algorithm (FDCA) is put forward based on one-time scan with cluster centers automatically determined by center set algorithm (CSA). A novel data similarity metric is designed for clustering data including numerical attributes and categorical attributes. CSA is designed to choose cluster centers from data object automatically which overcome the cluster centers setting difficulty in most clustering algorithms. The performance of the proposed method is verified through a series of experiments on ten mixed data sets in comparison with several other clustering algorithms in terms of the clustering purity, the efficiency, and the time complexity.
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Publication date: Available online 4 January 2018 Source: European Journal of Radiology Author(s): Peiyao Zhang, Jing Wang, Qin Xu, Zhen...
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Publication date: March 2017 Source: Free Radical Biology and Medicine, Volume 104 from #AlexandrosSfakianakis via Alexandros G.Sfak...
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Dtsch med Wochenschr DOI: 10.1055/s-0043-100054 Hintergrund und Fragestellung Ein etablierter Weg, die optimale Behandlung von Tumorpatien...
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ecancer is supporting #BowelCancerAwarenessMonth https://t.co/opXxCAAxzE from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inore...
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Linked Article: Maintz et al. Br J Dermatol 2017; 176:481–487 . from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader h...
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Angelina Jolie’s openness about her diagnosis has undoubtedly raised public awareness of the importance of inherited cancers. Although Desai...
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