Incremental association rule mining based on intermediate complete itemset

Other Title(s)

تعدين قواعد الجمعية التزايدي مستندة على مجموعة العناصر الكاملة المتوسطة

Dissertant

Aqra, Iyad Ahmad Izz al-Din

Thesis advisor

Thabtah, Fadi

Comitee Members

Ujan, Arafat
al-Zubaydi, Rashid A.

University

Philadelphia University

Faculty

Faculty of Information Technology

Department

Department of Computer Science

University Country

Jordan

Degree

Master

Degree Date

2013

English Abstract

-Association rule mining (ARM) is one of the most important tasks in data mining that has attracted a lot of attention in the research community.

The Apriori algorithm provides a creative and an intelligent way to find association rule on large database scale.

Apriori is one of the most important algorithms, which aims to explore association rules.

The main problem associated with Apriori is the multi scan database requires to find the rules when data gets updated every time.

This problem increases complexity when databases grow over time.

The discovered results from the original data are needed when mining the modified data set verifying knowledge obtained earlier.

Researchers have proposed many algorithms to deal with the incremental problem.

Especially in applications were changing databases constantly like banking application.

These algorithms created solution to the problem in an intelligent way, such as FUP, IMSC, MAAP algorithms.

When the existing incremental learning approaches are reviewed, we found some defects such as: (1) They do not take all data manipulation operations, specifically the update operation.

(2) These algorithms rescan database many times.

(3) Some of these algorithms discover knowledge without Frequency rate.

The proposed algorithm in this thesis is called Incremental Apriori (INAP), and it deals with the problems described above.

It is an incremental ARM that doesn’t need to rescan old database when gets update.

The algorithm takes all data manipulation operation including the modifying, deleting and adding transactions into account when mine the data set and without going back to iterate over the original dataset.

INAP algorithm allows us to extract knowledge with different thresholds (rule strength rate) every time without the needs to iterate over the original database meaning it solve the incremental problem in association rule.

Main Subjects

Mathematics

Topics

• Management
• Data protection
• Databases
• Security measures
• Data mining

No. of Pages

66

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-546129