Apriori algorithm for Arabic data using MapReduce

العناوين الأخرى

خوارزمية Apriori و استخدامها للنص العربي باستخدام نموذج MapReduce

مقدم أطروحة جامعية

al-Khudari, Ula Abd al-Nasir Hasan

مشرف أطروحة جامعية

Barakah, Ribhi Sulayman

أعضاء اللجنة

al-Halis, Ala Mustafa
al-Sayigh, Sana Wafa

الجامعة

الجامعة الإسلامية

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

تكنولوجيا المعلومات

دولة الجامعة

فلسطين (قطاع غزة)

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2015

الملخص الإنجليزي

Aprioi is the most popular algorithm that is used to extract frequent itemsets from large data sets where these frequent itemsets can be used to generate association rules.

Such rules are used as a basis for discovering knowledge such as detecting unknown relationships and producing results which can be used for decision making and prediction.

When the data size is very large, both memory use and computational cost are very expensive.

And in this case single processor’s memory and CPU resources are very limited which make the algorithm performance inefficient.

Parallel and distributed computing is effective for improving algorithm performance.

In our research we propose a parallel Apriori approach for large volume of Arabic text document using MapReduce with enhanced speedup and performance, Apriori algorithm that has been popular to collect the itemsets frequently occurred in order to compose Association Rule, MapReduce is a scalable data processing tool that enables to process a massive volume of data in parallel.

The experiments show that the parallel Apriori approach can process large volume of Arabic text efficiently on a MapReduce with 16 computers, which can significantly improve the execution time and speedup and also generate strong association rules.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

62

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-724514