استخراج الگوی افزایشی سودمندی بالا با ساختار لیست شاخص دار
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استخراج الگوی افزایشی سودمندی بالا با ساختار لیست شاخص دار

عنوان فارسی مقاله: روش کارآمد برای استخراج الگوی افزایشی سودمندی بالا با ساختار لیست شاخص دار
عنوان انگلیسی مقاله: Efficient approach for incremental high utility pattern mining with indexed list structure
مجله/کنفرانس: نسل آینده سیستم های کامپیوتری - Future Generation Computer Systems
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: رایانش ابری، مهندسی الگوریتم ها و محاسبات
کلمات کلیدی فارسی: داده کاوی، الگوهای سودمندی بالا، استخراج فزاینده، لیست های شاخص دار، استخراج سودمندی
کلمات کلیدی انگلیسی: Data mining، High utility patterns، Incremental mining، Indexed lists، Utility mining
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.future.2018.12.029
دانشگاه: Department of Computer Engineering, Sejong University, Seoul, Republic of Korea
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7/007 در سال 2018
شاخص H_index: 93 در سال 2019
شاخص SJR: 0/835 در سال 2018
شناسه ISSN: 0167-739X
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 19
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E11550
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Indexed list based incremental high utility pattern mining without candidate generation


4- Performance evaluation


5- Conclusions


References

نمونه متن انگلیسی مقاله

Abstract


Since traditional frequent pattern mining approaches assume that all the items in binary databases have the same importance regardless of their own features, they have difficulty in satisfying requirements of real world applications such as finding patterns with high profits. High utility pattern mining was proposed to deal with such an issue, and various relevant works have been researched. There have been demands for efficient solutions to find interesting knowledge from specific environments in which data accumulates continuously with the passage of time such as social network service, wireless network sensor data, etc. Although several algorithms have been devised to mine high utility patterns from incremental databases, they still have performance limitations in the process of generating a large number of candidate patterns and identifying actually useful results from the found candidates. In order to solve the problems, we propose a new algorithm for mining high utility patterns from incremental databases. The newly proposed data structures in a list form and mining techniques allow our approach to extract high utility patterns without generating any candidates. In addition, we suggest restructuring and pruning techniques that can process incremental data more efficiently. Experimental results on various real and synthetic datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory, and scalability.


Introduction


As one of various data mining techniques [1,2], pattern mining has played an important role in finding useful knowledge hidden from large amounts of data [3–10]. As a fundamental research topic of pattern mining, frequent pattern mining identifies all of the patterns that appear more frequently than a user-defined threshold from a given binary database [3,11]. A binary database is composed of a number of transactions, each of which has multiple items. Then, each item is denoted as 0 (absence) or 1 (presence). Traditional approaches assume that all the items in such a binary type of data have the same importance regardless of their own characteristics. Hence, they mine patterns simply considering item frequencies with various real-world features ignored. However, each item in real-world databases has its own importance as well as quantity information that may exceed 1. In this regard, frequent pattern mining does not satisfy the need to find patterns with high profits in real-world applications such as market analysis. High utility pattern mining has been researched as an important topic in the pattern mining area in order to deal with such problems [12– 15]. High utility pattern mining ([16,17]; Yun, Ryang, & Ryu, 2016) considers non-binary features and the distinct importance of items to the mining process, which has made it possible to extract more useful patterns, called high utility patterns, satisfying a user-given threshold. In recent years, large-scale data have been generated continually in various real-world applications such as web data, wireless network sensor data, and social network service. As a result, this trend has increased the importance of data analysis.

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