کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4943482 | 1437633 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel approach for mining maximal frequent patterns
ترجمه فارسی عنوان
رویکرد جدید برای الگوهای مکرر حداکثر معدن
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank-k FPs. Therefore, this paper uses the N-list structure for mining MFPs. A pruning technique is also proposed to prune branches to reduce the search space. This technique is applied to an algorithm called INLA-MFP (improved N-list-based algorithm for mining maximal frequent patterns) for mining MFPs. Experiments were conducted to evaluate the effectiveness of the proposed algorithm. The experimental results show that INLA-MFP outperforms two state-of-the-art algorithms for mining MFPs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 73, 1 May 2017, Pages 178-186
Journal: Expert Systems with Applications - Volume 73, 1 May 2017, Pages 178-186
نویسندگان
Bay Vo, Sang Pham, Tuong Le, Deng Zhi-Hong,