Developing an efficient mechanism for mining association rules in small large transactional databases

مقدم أطروحة جامعية

al-Sharman, Husam Muhammad

مشرف أطروحة جامعية

al-Bahadili, Husayn

أعضاء اللجنة

al-Shammari, Husayn Hadi Uwayyid
al-Shaykh, Asim A. R.
al-Lahham, Muhammad Ismail Abd al-Rasul

الجامعة

الأكاديمية العربية للعلوم المالية و المصرفية

الكلية

كلية نظم و تكنولوجيا المعلومات

القسم الأكاديمي

قسم نظم المعلومات الحاسوبية

دولة الجامعة

الأردن

الدرجة العلمية

دكتوراه

تاريخ الدرجة العلمية

2012

الملخص الإنجليزي

This thesis presents a description of a newly developed high performance association rule mining algorithm that basically combines the well-known Apriori association rule mining algorithm with a compact mechanism, namely, the Dynamic Compact Grouping Item sets (DCGI).

DCGI is specially designed for optimizing online and very large transactional databases.

Therefore, this new algorithm is called Online Dynamic Grouping Association Rule Mining (ODGARM) algorithm.

ODGARM has a number of advantages over existing algorithms, such as: the first to provide efficient and accurate online dynamic grouping association rule mining on transactional databases of various sizes, allow generating frequent patterns quickly by skipping the repetitive database scan and reducing a great amount of time per database scan; reduce the search space (candidate's item sets) and discover only those rules which can be interesting for the user; reduce the memory requirements to store a huge number of useless candidates; and enable good diversification of the search space and in many cases decreases the time needed for the algorithm to generate a set of large frequent item sets.

The main corner stone of ODGARM are: inclusion of DCGI as a compact pre-processing mechanism, penalizing rules with few item sets using weight thresholds, inclusion of a new mechanism supported with developed mathematical model to prevent eliminating good item sets that might be part of the best solutions we are looking for, avoid one-at-a-time transaction processing and retrieval from the database, and avoid spending time improving low starting quality solution.

In order to evaluate and compare the performance of ODGARM with other algorithms in the literature, four scenarios are simulated using synthetic and real datasets.

The first three scenarios investigate the effect of input parameters, such as: minimum support, number of items in item set, and number of database transactions.

The forth scenario presents a comparison between the performance of ODGARM against the performance of other algorithms found in the literature (e.g., Apriori, Apriori-Tad, Apriori Hybrid, FP-Growth, and Partition).

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• معالجة البيانات
• قواعد البيانات
• التنقيب في البيانات

عدد الصفحات

84

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-306659