کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11021201 | 1715030 | 2019 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns
ترجمه فارسی عنوان
یک نمادگرایی مجموعه ای از مفهوم تکاملی چند هدفه برای الگوهای مختلف بهره برداری از معادن بالا
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
How to discover top-k patterns with the largest utility values, namely, mining top-k high utility patterns, is a hot topic in data mining. However, most of the existing works for mining top-k high utility patterns consider each pattern separately during the mining process, thus many mined patterns are highly similar and lack diversity. In this paper, we propose to mine top-k high utility patterns with high diversity for enhancing users'satisfaction in recommendation. Specifically, we first introduce a simple measure of coverage to quantify the diversity of the whole set, that is, the top-k patterns as a complete entity. Then we propose an i ndexed s et r epresentation based m ulti-o bjective e volutionary a pproach named ISR-MOEA to mine diversified top-k high utility patterns, due to the fact that the two measures utility and coverage are conflicting. In ISR-MOEA, an indexed set individual representation scheme is suggested for fast encoding and decoding the top-k pattern set. Experimental results on six real-world and two synthetic datasets demonstrate the effectiveness of the proposed approach. The proposed approach can obtain several groups of top-k pattern set with different trade-offs between utility and diversity in only one run, which would further enhance the satisfaction of users.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 77, January 2019, Pages 9-20
Journal: Engineering Applications of Artificial Intelligence - Volume 77, January 2019, Pages 9-20
نویسندگان
Lei Zhang, Shangshang Yang, Xinpeng Wu, Fan Cheng, Ying Xie, Zhiting Lin,