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https://open.uns.ac.rs/handle/123456789/2086
Nаziv: | A hybrid clustering approach for diagnosing medical diseases | Аutоri: | Simić, Maja Banković Z. Simić, Dragan Simić, Maja |
Dаtum izdаvаnjа: | 1-јан-2018 | Čаsоpis: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Sažetak: | © Springer International Publishing AG, part of Springer Nature 2018. Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied extensively in various research fields, including data mining, machine learning, pattern recognition, and in scientific, engineering, social, economic, and biomedical data analysis. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimates clustering algorithm for diagnosing medical diseases. The proposed hybrid system is first tested on well-known Iris data set and then on three data sets for diagnosing medical diseases from UCI data repository. | URI: | https://open.uns.ac.rs/handle/123456789/2086 | ISBN: | 9783319926384 | ISSN: | 3029743 | DOI: | 10.1007/978-3-319-92639-1_62 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
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