کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9663712 | 1446239 | 2005 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mining generalized knowledge from ordered data through attribute-oriented induction techniques
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
The attribute-oriented induction (AOI for short) method is one of the most important data mining methods. The input of the AOI method contains a relational table and a concept tree (concept hierarchy) for each attribute, and the output is a small relation summarizing the general characteristics of the task-relevant data. Although AOI is very useful for inducing general characteristics, it has the limitation that it can only be applied to relational data, where there is no order among the data items. If the data are ordered, the existing AOI methods are unable to find the generalized knowledge. In view of this weakness, this paper proposes a dynamic programming algorithm, based on AOI techniques, to find generalized knowledge from an ordered list of data. By using the algorithm, we can discover a sequence of K generalized tuples describing the general characteristics of different segments of data along the list, where K is a parameter specified by users.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 166, Issue 1, 1 October 2005, Pages 221-245
Journal: European Journal of Operational Research - Volume 166, Issue 1, 1 October 2005, Pages 221-245
نویسندگان
Yen-Liang Chen, Ching-Cheng Shen,