Predictive trend mining for social network analysis

This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established

An Analysis of Energy Mix in Peninsular Malaysia in Line with the Malaysia's Existing Energy Policy

This paper considers dynamic changes of energy-mix available in Peninsular Malaysia with respect to the Malaysia’s energy policies and evaluates these on experimental basis. This research applied a Data Mining approach; Self Organizing Map (SOM) Algorithm for trend cluster analysis time series data. The approach can provide a number of capabilities to uncover relationships between data attributes, uncover relationships between observations, predict the outcome of future observations and learn how to best react to situations through trial and error by using reinforcement learning. Based on the experiment, the test results have shown that the application is able to accommodate large sets of data and produced the trend lines graphs thus at the same time, a clearer picture of scenarios and the latest trend of energy mix applied in Peninsular Malaysia were successfully obtained; it is shown that Malaysian government should increase the execution and improvement in the realization and implementation of energy policy in Malaysia. Besides, Malaysia still has a lot of potential in order to fully utilise renewable energy resources.