Session:

Knowledge Representation, Decision Support and Expert Systems
Tuesday, March 02, 2004, 17.55 – 18.15

Session Chair:
Vice Chair:

A. Dobnikar
M. Savoji

Paper Title:

Fast Efficient Association Rule Mining from Web Data

Author(s):

W. Hussein, Ain shams University, Egypt
O.H. Karam, Ain shams University, Egypt
A. M. Hamad, Ain shams University, Egypt

Abstract:

The need for the analysis of the behavior of users on the World Wide Web motivated the use of data mining techniques for the discovery of traversal patterns. These patterns are usually expressed in the form of association rules. In this paper, we suggest a graph representation of the transactions database to assist with its division into a set of databases each containing fewer transactions and items. The runtime of the Apriori algorithm was compared when run on both the original and the divided databases. The division of the database was shown to improve the runtime by an average of 43.45% while maintaining the same results. Interestingness measures were also introduced as a way to improve the quality of the resulting rules. Introducing interestingness measures to the division process improved the average precision of the algorithm by a minimum of 15.5%.

Key Words: Web Usage Mining, Data Mining, Association Rules, Interestingness Measures.