The Mining and Analysis of Data with Mixed Attribute Types

Abstract

Ed Wakelam, Neil Davey, Yi Sun, Amanda Jefferies, Parimala Alva, and Alex Hocking, ‘The Mining and Analysis of Data with Mixed Attribute Types’, paper presented at the IMMM 2016: Sixth International Conference on Advances in Information Mining and Management, 22 May 2016 – 26 May 2016, Valencia, Spain. Published by IARIA XPS Press, Archived in the free access ThinkMind™ Digital Library. Available online at http://www.thinkmind.org/index.php?view=article&articleid=immm_2016_3_20_50067 © IARIA, 2016Mining and analysis of large data sets has become a major contributor to the exploitation of Artificial Intelligence in a wide range of real life challenges, including education, business intelligence and research. In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in this important area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. In this paper, we summarise our progress in applying a combination of what we believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs, followed by numeric data analysis, providing the opportunity to focus on promising correlations for deeper analysis.Final Accepted Versio