کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6861614 | 1439255 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mining strong symbiotic patterns hidden in spatial prevalent co-location patterns
ترجمه فارسی عنوان
معادن الگوهای همزیستی قوی مخفی در الگوهای مکان همپوشانی شایع در معماری
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
کاوش داده های فضایی، الگوهای همبستگی مکانی، الگوهای همزیستی قوی، پایگاه داده های فضایی دینامیک،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Spatial co-location patterns represent the subsets of spatial features which are frequently located together in a geographic space. Spatial co-location pattern mining has been a research hot in recent years. However, maybe the features in a prevalent co-location pattern further have more interesting relationships such as symbiotic relationships, competitive relationships or causal relationships. This paper mines symbiotic relationships implied in prevalent co-location patterns from dynamic spatial databases. Firstly, after analyzing the existed definition of symbiotic patterns, a criterion of judging strong symbiotic patterns is proposed. Secondly, a novel algorithm to mine strong symbiotic patterns from prevalent co-location patterns is presented, named basic algorithm. Third, for improving the efficiency of the basic algorithm, an improved algorithm which integrates two expensive operations of the basic algorithm into together, and a pruning strategy with two pruning lemmas are presented. The experiments evaluate the effectiveness and efficiency of the proposed algorithms with “realâ¯+â¯synthetic” data sets and the results show that strong symbiotic patterns are more concise and actionable compared to traditional prevalent co-location patterns.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 146, 15 April 2018, Pages 190-202
Journal: Knowledge-Based Systems - Volume 146, 15 April 2018, Pages 190-202
نویسندگان
Junli Lu, Lizhen Wang, Yuan Fang, Jiasong Zhao,