Abstract
Discovering Web Usage Pattern Using Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
Rolysent K. Paredes1, Ruji P. Medina2, Ariel M. Sison3
With the considerable amount of data collected from the Information
Technology systems including computer networks, many institutions and
organizations are considering the benefit in discovering user patterns. This
paper presents the processes in developing the enhanced algorithm for Linear
Discriminant Analysis via Generalized Singular Value Decomposition
(LDA/GSVD) which primarily functions to discover web usage patterns.
Preprocessing, class labeling using Self-Organizing Map (SOM), computation
of the feature subspaces, and the training of the Artificial Neural Network
(ANN) architecture are stages in developing the algorithm. After the
development, this neural network-based algorithm was used to discover
patterns from the university’s proxy servers’ weblogs. Results showed an
appropriate classification of the network users’ web usage with the improved
algorithm. Hence, results revealed that more users in the campus whether
students or employees are utilizing the internet for noneducational rather than
educational. Also, simulation results showed that the enhanced algorithm
outperformed the current LDA/GSVD algorithm up to 50% on computational
cost. Through this approach, IT managers will be guided to make better plans
to optimize the utilization of the internet. Moreover, this proposed technique
will benefit not only the educational institutions but also other organizations
which mostly need pattern discovery to their systems’ enormous data.
KEYWORDS: computer, development, internet, network, weblogs
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