Enhanced Apriori algorithms’ : discovering patterns using the cloud or probabilistic range approach

Dissertant

Ali, Mir Dawar

Thesis advisor

Atum, Jalal Yusuf

Comitee Members

Awajan, Arafat
Ubayd, Nadim
al-Majali, Sufyan

University

Princess Sumaya University for Technology

Faculty

King Hussein Faculty for Computing Sciences

Department

Department of Computer Sciences

University Country

Jordan

Degree

Master

Degree Date

2015

English Abstract

Finding frequent patterns using ‘Apriori’ algorithm has been an integral part of knowledge discovery science.

This algorithm is costly on large search spaces and therefore it is executed mainly on a limited portion of the search space to catch up with time and provide instant results.

This limitation effects the quality and the number of the derived patterns and therefore a need to modify the exisiting algorithm to come up with innovative algorithms’ firstly capable to be driven over multiple cores in the clouds to discover patterns over extensively large search space in a relatively faster time without compromising on the quality of the deliverables and secondly with a probabilistic range approach. Applying these core derivative algorithms’ on huge data resources already available for different business domains over the web, we should v be able to see patterns within this data and then make them useful based on the frequency measures inorder to later enhance and continuously learn to improve the decisions made based on these generated patterns .

Main Subjects

Information Technology and Computer Science

Topics

• Algorithms

No. of Pages

54

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-651035