إن استخدام الطرق التقليدية لتحليل الكميات الهائلة من مجموعات البيانات لا
يساعد على اكتشاف أنماط معرفية جديدة تدعم عملية اتخاذ القرار. و بالتالي إن الغرض من هذه المقالة تصميم نظام تحليل مرئي يدعم عملية تحليل مجموعات البيانات من خلال استخدام التحليل الآلي الذي يتضمن العديد من التقنيات مثل (clustering) التجميع, و التنصنيف (classification),
وَ قاعدة (association Rule) الإرتباط, و تقنيات الإظهار المتمثلة بعملية استكشاف البيانات المرئية، و من ثم المقارنة مع تقنيات الإظهار الأخرى لمجموعات البيانات و تقييم نظام الإظهار المقترح.
The use of traditional methods to analyze massive amounts of
data sets is not conducive to the discovery of new knowledge
patterns supports the decision-making process So the purpose
of this article is designed visual analysis system that supports
analysis of data sets through the use of automated analysis,
which includes many of the techniques such as assembly
process (clustering) and Altnsnev (classification) and the
correlation base (association Rule) And the process of visual
data exploration techniques Manifesting, and then the
comparison with other data sets manifestation techniques and
evaluation of the proposed Manifesting system.
References used
Y. Zhu. Measuring e_ective data visualization. Advances in Visual Computing, pages 652–661, 2007
C. Ware. Visual thinking for design. Morgan Kaufmann, 2008
L. Voinea and A. Telea. Case study: Visual analytics in software product assessments. In In Proceedings of IEEE VISSOFT, pages 65–72, 2009
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