Scalable parallel SOM learning for web user profiles

Extraction of social networks from log files and social network analysis then requires the usage of data mining methods focused on areas such as data clustering or pattern mining. Our research is focused on log files where one log file attribute is an originator of the recorded activity and the originator is also a person. Hence, based on the similar attributes of people, we are able to construct models which explain certain aspects of a persons behaviour. Moreover, we can extract user profiles based on person behaviour in the web applications. Working with large user profiles, usually acquired from the web log files, the dimension reduction from original high dimensional space to 2D space could be done using Kohonen SOM. The SOM also provides clusters of similar web profiles of particular users. For large SOM learning it is appropriate to use parallel computing environment. Our version of scalable parallel SOM learning algorithm and experiment with web user profiles are presented in this paper.