Andrew Ware

 

Title Mining for Data’s Golden Nuggets
Abstract

Machine learning, data mining, and predictive analytics are all terms that can be used to mean the same thing. In essence they refer to the use of relatively powerful computers to work through large volumes of data, which can be diverse in nature, in order to detect hitherto hidden patterns and correlations that can, in many cases, be used to predict future patterns and correlations. Usually these predictions are at the micro rather than the macro level of activity. For example, the aim of deploying the techniques might be to predict whether a particular individual will develop a certain disease, buy a particular product, or like a certain type of music. These micro level predictions contrast to macro level predictions that are more likely to try and determine such things as the percentage of people that will develop a certain disease, buy a particular product, or like a certain type of music. The techniques are having an increasingly profound impact on the way businesses, healthcare providers, and all manner of other entities are going about their daily business. The talk will investigate some of the predominant paradigms associated with the subject and highlight the strengths and weaknesses that they possess. Success stories will be examined and failures analysed. In short, the aim will be to help those interested in the subject plot a path that helps ensure that the useful nuggets of information hidden deeply within data are located and understood in a timely and proficient manner.

Bio

Andrew Ware is a Professor of Computing at the University of South Wales where he is engaged in both teaching and research. His main research area is Applied Artificial Intelligence. Professor Ware is involved with projects in Pakistan, Singapore, Norway and the USA. He is also Regional Director of Technocamps, an innovative and progressive initiative aimed at increasing the number of young people studying Computer Science at school and university. Andrew has successfully supervised nearly 30 PhD students. He is a Fellow of the British Computer Society and Senior Fellow of the Higher Education Academy.