Enhancing Web Data Mining

Abstract

An enormous production of databases in almost every area of human endeavor particularly through web has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. The aim of this study is to study the predictive ability of Factor Analysis a web mining technique to prevent voting, averaging, stack generalization, meta- learning and thus saving much of our time in choosing the right technique for right kind of underlying dataset. This chapter compares the three factor based techniques viz. principal component regression (PCR), Generalized Least Square (GLS) Regression, and Maximum Likelihood Regression (MLR) method and explores their predictive ability on theoretical as well as on experimental basis. All the three factor based techniques have been compared using the necessary conditions for forecasting like R-Square, Adjusted R-Square, F-Test, JB (Jarque-Bera) test of normality. This study can be further explored and enhanced using sufficient conditions for forecasting like Theil's Inequality coefficient (TIC), and Janur Quotient (JQ).