Visualizing discovered knowledge in databases mining

العناوين الأخرى

تخيل المعارف المكتشفة في تنقيب قواعد البيانات

مقدم أطروحة جامعية

Abu Khudayr, Yasir Ishaq Dawud

مشرف أطروحة جامعية

al-Ani, Muzhir Shaban

أعضاء اللجنة

al-Hamami, Ala Husayn
Aqil, Misbah Jumah

الجامعة

جامعة عمان العربية

الكلية

كلية العلوم الحاسوبية و المعلوماتية

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2011

الملخص الإنجليزي

Extracting knowledge from educational data delivers advanced business intelligence and web discovery solutions.

The education sector in Jordan faces a significant challenge to growth while Jordan’s business and government leaders ascertain the country’s differentiated competitive advantage relative to other regional and global players.

In order to focus marketing and promotion efforts—as well as to develop a strategy to expand and diversify Jordan’s higher education offering, there is a need to better understand the availability and capacity of the labor supply side that will match the demand expectations of companies seeking to locate higher-and higher education operations within Jordan.

One of the biggest challenges that higher education faces today is predicting the paths of students.

Institutions would like to know, for example, which universities; its students are the most employed.

Are some students more likely to work than others? In addition to this challenge, statistics are extremely important for development plans, forecasting trends, and for business process outsourcing.

We propose an approach for classifying the most suitable major based on historical data, based on decision tree method, and visualizing knowledge discovered from educational database, by adopting ID3 decision tree algorithm and incorporating Dashboard concept for Jordanian higher education institution.

Experiments were run for educational databases associated with different institutions, beginning with Ministry of Higher Education (MOHE), National Information Technology Center (NITC), public and private universities, and Social Security Corporation in Jordan.

In this thesis we are trying to find out classes of workers, using ID3 decision tree algorithm a classification algorithm, that produce a tree that is used to classify future samples of data, in order to help companies, to extract classes of students who are most probably to work.

In order to achieve our goals, we process various sources of databases (a total of 250000 records), and partitioned it into training and testing data, and finally applying ID3 decision tree algorithm.

Additionally, a group of statistical reports are implemented, in order to help decision makers to make decisions.

The detected or inferred information should increase our knowledge about each major, university, and faculty.

The resulted decision tree will be used to apply the testing data on.

A set of structural models from different universities, and majors have been in our database.

Different structural models have been proposed, with statistical reports.

The average accuracy of the models is close to (79%) using a lift chart.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الإدارة
• قواعد البيانات
• التنقيب في البيانات

عدد الصفحات

112

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-525952