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Improving Efficiency Apriori Algorithm by Reduction of candidate itemsets

تحسين فعالية خوارزمية الأسبقية بتخفيض توليد مجموعات بنود البيانات المُرشحة

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 Publication date 2017
and research's language is العربية
 Created by Shamra Editor




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Association Rules is an important field in Data Mining, which is used to discover useful knowledge from a massive databases. Association Rules have been used to extract the information from the database transactions, and Apriori Algorithm is a practical application for Association Rules and it is used to find frequent itemsets from database transactions. In this paper, we present a new improving on Apriori Algorithm by reduction generating of candidate itemsets and this leads to improving efficiency Apriori Algorithm.


Artificial intelligence review:
Research summary
تتناول هذه الورقة البحثية تحسين خوارزمية الأسبقية المستخدمة في التنقيب عن البيانات، بهدف زيادة فعاليتها من خلال تقليل توليد مجموعات بنود البيانات المُرشّحة. تُعتبر قواعد الارتباط أداة هامة لاستخلاص المعرفة من قواعد البيانات الضخمة، وتُستخدم خوارزمية الأسبقية لاكتشاف مجموعات البيانات المتكررة في هذه القواعد. يهدف البحث إلى تقديم تحسين جديد للخوارزمية من خلال تقليل عدد مجموعات بنود البيانات المُرشّحة، مما يؤدي إلى زيادة سرعة الأداء وتقليل الزمن المستهلك. تم بناء برنامج بسيط يعتمد على الخوارزمية المُحسّنة للتنبؤ بالمنتجات التي قد تظهر في فاتورة المشتريات في مراكز التسويق. أظهرت النتائج أن الخوارزمية المُحسّنة ساعدت في تقليل عدد مجموعات بنود البيانات المُرشّحة، مما أدى إلى تحسين الأداء بشكل ملحوظ.
Critical review
تُعد هذه الورقة البحثية خطوة هامة نحو تحسين خوارزمية الأسبقية، إلا أنه كان من الممكن تقديم تحليل أكثر تفصيلاً حول كيفية تأثير التحسينات على الأداء في مختلف سيناريوهات قواعد البيانات. كما أن الدراسة قد استفادت من تقديم مقارنات مع خوارزميات أخرى في مجال التنقيب عن البيانات لتوضيح مدى تفوق الخوارزمية المُحسّنة. من الجيد أيضاً تضمين تجارب عملية على قواعد بيانات حقيقية بدلاً من الأمثلة النظرية فقط، مما يعزز من مصداقية النتائج ويزيد من تطبيقاتها العملية.
Questions related to the research
  1. ما هو الهدف الرئيسي من تحسين خوارزمية الأسبقية في هذا البحث؟

    الهدف الرئيسي هو زيادة فعالية خوارزمية الأسبقية من خلال تقليل توليد مجموعات بنود البيانات المُرشّحة، مما يؤدي إلى تحسين سرعة الأداء وتقليل الزمن المستهلك.

  2. كيف تم تحسين خوارزمية الأسبقية في هذه الدراسة؟

    تم تحسين الخوارزمية من خلال تقليل عدد مجموعات بنود البيانات المُرشّحة عبر خطوات محددة تشمل ضغط قاعدة البيانات وتوليد المجموعات المُرشّحة بشكل أكثر فعالية.

  3. ما هي الأدوات واللغات البرمجية المستخدمة في بناء البرنامج البسيط الذي يعتمد على الخوارزمية المُحسّنة؟

    تم استخدام برنامج NetBeans IDE 8.1 لتصميم واجهة البرنامج، ولغة الجافا (Java) لكتابة التعليمات البرمجية.

  4. ما هي الفائدة العملية من استخدام خوارزمية الأسبقية المُحسّنة في مجال التسويق؟

    تساعد خوارزمية الأسبقية المُحسّنة أصحاب المحلات ومراكز التسويق في عرض المنتجات التي يقوم الزبائن بشرائها بجانب بعضها البعض، مما يؤدي إلى زيادة الربح.


References used
LIU B, 2006- Web Data Mining. Springer-Verlag New York
HAN J and KAMBER M, 2006- Data Mining:Concepts and Techniques. Second ed, Elsevier Inc, United States of America
JAISHREE S, HARI R and SODHI J 2013 Improving Efficioncy of Apriori Algorithm Using Transaction Reduction International Journal of Scientific and Research Publications,Vol.3, Issue 1
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