Application of data mining techniques to predict internet usage consumption for personal objectives in the work place

Abstract

Internet usage by employees for personal of inappropriate purposes can directly impact the productivity and efficiency of the organization. This translates to lost time, opportunity and money. In this research we use a data mining technique to build an internet usage consumption model by applying two different methods to web server log data: 1) decision trees based upon a C4.5 algorithm and 2) Multilayer perceptrons. The overall results obtained indicate that multilayer perceptrons with the cross validation have higher performance in classifying and predicting employee web browsing habits than decision trees. This data mining technique can therefore be a good candidate for helping organizations make more effective evaluation of their human and computer resources