کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
416631 | 681388 | 2007 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
A method to analyse links between binary attributes in a large sparse data set is proposed. Initially the variables are clustered to obtain homogeneous clusters of attributes. Association rules are then mined in each cluster. A graphical comparison of some rule relevancy indexes is presented. It is used to extract best rules depending on the application concerned. The proposed methodology is illustrated by an industrial application from the automotive industry with more than 80 000 vehicles each described by more than 3000 rare attributes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 1, 15 September 2007, Pages 596–613
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 1, 15 September 2007, Pages 596–613
نویسندگان
Marie Plasse, Ndeye Niang, Gilbert Saporta, Alexandre Villeminot, Laurent Leblond,