کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4945274 | 1438416 | 2017 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Possibilistic and fuzzy clustering methods for robust analysis of non-precise data
ترجمه فارسی عنوان
روشهای خوشه بندی مثبت و فازی برای تحلیل قوی داده های غیر دقیق
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کلمات کلیدی
اطلاعات نامطلوب، نیرومندی، خوشه بندی فازی، خوشه بندی مثبت، اعتبار خوشه ای،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This work focuses on robust clustering of data affected by imprecision. The imprecision is managed in terms of fuzzy sets. The clustering process is based on the fuzzy and possibilistic approaches. In both approaches the observations are assigned to the clusters by means of membership degrees. In fuzzy clustering the membership degrees express the degrees of sharing of the observations to the clusters. In contrast, in possibilistic clustering the membership degrees are degrees of typicality. These two sources of information are complementary because the former helps to discover the best fuzzy partition of the observations while the latter reflects how well the observations are described by the centroids and, therefore, is helpful to identify outliers. First, a fully possibilistic k-means clustering procedure is suggested. Then, in order to exploit the benefits of both the approaches, a joint possibilistic and fuzzy clustering method for fuzzy data is proposed. A selection procedure for choosing the parameters of the new clustering method is introduced. The effectiveness of the proposal is investigated by means of simulated and real-life data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 88, September 2017, Pages 23-38
Journal: International Journal of Approximate Reasoning - Volume 88, September 2017, Pages 23-38
نویسندگان
Maria Brigida Ferraro, Paolo Giordani,