کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6948391 | 1451038 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Change detection model for sequential cause-and-effect relationships
ترجمه فارسی عنوان
تغییر مدل تشخیص برای روابط پیوسته علت و اثر
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کلمات کلیدی
داده کاوی، تغییر معادن، الگوهای متوالی طبقه بندی شده، ارتباطات علت و معلول، اطلاعات بزرگ،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سیستم های اطلاعاتی
چکیده انگلیسی
Detecting changes of behaviors or events is crucial when updating existing knowledge in a dynamic business environment. Currently, data analysts can immediately collect data and easily access existing knowledge. However, that knowledge can also rapidly become outdated. This study discusses a form of knowledge, classifiable sequential patterns (CSPs), defined as s â c, where s is a temporal sequence; c is a class label; and “â” is a sign which implies the sequential relationships between s (cause) and c (effect). If the CSP evolves into another, and the new knowledge is not updated, decision-makers would continue to work with the obsolete CSP. To the authors' knowledge, no study has addressed the topic of change mining in CSPs. To address this research gap, this study proposes a novel change-mining model, SeqClassChange, to identify changes in CSPs. Experiments were conducted with a real-world dataset to evaluate the proposed model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Decision Support Systems - Volume 106, February 2018, Pages 30-43
Journal: Decision Support Systems - Volume 106, February 2018, Pages 30-43
نویسندگان
Tony Cheng-Kui Huang, Pu-Tai Yang, Jen-Hung Teng,