Data Mining Techniques for Web Mining: A Survey

TLDR

Data mining is a computational process that searches, extracts, and analyzes patterns in large datasets using AI, machine learning, statistics, and database techniques, and its evolving form, web mining, automatically discovers valuable information from web records and services. This paper surveys data mining techniques applied to web mining to provide a comprehensive overview of the field.

Abstract

The data mining (DM) is the computational process that consists of searching, extracting, and analyzing patterns in large data sets, including methods at the intersection of artificial intelligence, machine learning, statistics, and database schemes. Specifically, its primary goal is to extract information from a raw data set and transform it into an expected structure for further use. Moreover, an evolving perspective of DM is web mining (WM), which refers to the whole of DM and related routines. It is used to discover and extract information from web records and services automatically, that is, WM’s purpose is to obtain valuable data from the World Wide Web. Due to its importance, a survey about DM techniques in WM is necessary, as performed in this paper.