The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model

Social networks have increased in popularity and play an important role in people's life nowadays. Hundreds of millions of people participate in social networks and the number is growing day by day. Social networks have become a useful tool and help people in every field of life such as in education, politics and business. Social networks give people the idea of knowing and interacting with each other, experiencing the power of sharing and being connected with people from different places and countries. The purpose of this study is to analyse the behaviour of actors in a network, the graph and the relationship between actors in social networks. The researcher expects to use the technique of Social Network Analysis with Organisation Risk Analyser (ORA) tool to analyse the data. Three different types of dataset are analysed in the form of network visualisation and centrality measurement. The results reveal the hidden relationships and clusters in the network, and indicate which nodes provide better performance for each centrality measure.