کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10322599 | 660862 | 2012 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Constraint graph-based frequent pattern updating from temporal databases
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
There have been many kinds of association rule mining (ARM) algorithms, e.g., Apriori and FP-tree, to discover meaningful frequent patterns from a large dataset. Particularly, it is more difficult for such ARM algorithms to be applied for temporal databases which are continuously changing over time. Such algorithms are generally based on repeating time-consuming tasks, e.g., scanning databases. To deal with this problem, in this paper, we propose a constraint graph-based method for maintaining frequent patterns (FP) discovered from the temporal databases. Particularly, the constraint graph, which is represented as a set of constraint between two items, can be established by temporal persistency of the patterns. It means that some patterns can be used to build the constraint graph, when the patterns have been shown in a set of the FP. Two types of constraints can be generated by users and adaptation. Based on our scheme, we find that a large number of dataset has been efficiently reduced during mining process and the gathering information while updating.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 3, 15 February 2012, Pages 3169-3173
Journal: Expert Systems with Applications - Volume 39, Issue 3, 15 February 2012, Pages 3169-3173
نویسندگان
Jason J. Jung,