Data Mining (DM) and Knowledge Discovery (KD) are concerned with the extraction of useful knowledge from large quantities of more or less structured information. With the continued growth in large data sets and the inability of manual analytical techniques to cope with such volumes, data mining algorithms and knowledge discovery processes and frameworks have emerged as potential solutions. Specifically the topic will cover: Introduction - The role of common sense, trends in information management, fundamental ideas, developing data mining algorithms, applications of knowledge discovery, future directions in DMKD. Data mining techniques - association rule mining, clustering algorithms, classification and prediction, sequential pattern mining, graph mining, text mining, higher order data mining, visualisation techniques, spatial data mining, temporal and longitudinal data mining, interestingness, web mining, ethics in data mining, knowledge discovery frameworks, research methods used in data mining and knowledge discovery.
On completion of this topic, students will have gained knowledge in: