کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9651016 | 666666 | 2005 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining](/preview/png/9651016.png)
چکیده انگلیسی
The problem of mining multidimensional inter-transactional association rules was recently introduced in [ACM Trans. Inform. Syst. 18(4) (2000) 423; Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, p. 12:1]. It extends the scope of mining association rules from traditional single-dimensional intra-transactional associations to multidimensional inter-transactional associations. Inter-transactional association rules can represent not only the associations of items happening within transactions as traditional intra-transactional association rules do, but also the associations of items among different transactions under a multidimensional context. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. In this paper, we extend the previous problem definition based on context expansion, and present a more general form of association rules, named generalized multidimensional inter-transactional association rules. An algorithm for mining such generalized inter-transactional association rules is presented by extension of a priori. We report our experiments on applying the algorithm to both real-life and synthetic data sets. Empirical evaluation shows that with the generalized inter-transactional association rules, more comprehensive and interesting association relationships can be detected from data sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 172, Issues 3â4, 9 June 2005, Pages 361-395
Journal: Information Sciences - Volume 172, Issues 3â4, 9 June 2005, Pages 361-395
نویسندگان
Qing Li, Ling Feng, Allan Wong,