Relationship Analysis of Keyword and Chapter in Malay-Translated Tafseer of Al-Quran

A number of studies have gained popularity to study the unseen knowledge categories and relationship of subject matters discussed in the Al-Quran or the Tafseer. This research investigates the relationships between verses and chapters at the keyword level in a Malay translated Tafseer. A combination technique of text mining and network analysis is developed to discover non-trivial patterns and relationships of verses and chapters in the Tafseer. This is achieved through keyword extraction, keyword-chapter relationship discovery and keyword- chapter network analysis. A total of 130 keywords were extracted from six chapters in the Tafseer. The keywords and their relative importance to a chapter are computed using term weighting. A network analysis map was generated to visualize and analyze the relationship between keyword and chapter in the Tafseer. The relationship between the verses and chapters at the keyword level are successfully portrayed through the combination technique of text mining and network analysis. The novelty of this approach lies in the discovery of the relationships between verses and chapters that is useful for grouping related chapters together

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.

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

This paper describes an approach to temporal pattern mining using the concept of user defined temporal prototypes to define the nature of the trends of interests. The temporal patterns are defined in terms of sequences of support values associated with identified frequent patterns. The prototypes are defined mathematically so that they can be mapped onto the temporal patterns. The focus for the advocated temporal pattern mining process is a large longitudinal patient database collected as part of a diabetic retinopathy screening programme, The data set is, in itself, also of interest as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The diabetic retinopathy application, the data warehousing and cleaning process, and the frequent pattern mining procedure (together with the application of the prototype concept) are all described in the paper. An evaluation of the frequent pattern mining process is also presented