Web Access Log Mining

Analysis of web visitor's access patterns can lead to benefits in a wide range of areas such as decision support and website restructuring. Data mining techniques can be used to find access patterns hidden inside huge volumes of web access data. The goal of this Paper is to determine whether there are any such patterns in the web access data for the computer science Website of GGSIP University. The data mining techniques of classification, association rules, and clustering and attribute selection were used with four different feature sets. Three major patterns were discovered: (1) Visitors from India generally visit the root page while visitors from outside India do not. The most likely reason for this is that visitors from outside India use search engines that direct them to specific pages. However, some (2) Visitors from outside India visit the root page and pages about post graduate programs. This suggests that these visitors are mostly interested in post graduate studies. (3) Visitors from other educational institutions tend to visit pages related to staff contact information while other visitors tend to access career and industry related information. During the course of the investigation, it was found that there were a significant number of long transactions. The long transactions were analyses manually and it was found that visitors in a significant number of transactions access information about different programs offered and towards the end of their visit they look for the information brochure of one program.