An advanced and up-to-date knowledge in the field of Knowledge discovery process. Designing & Implementing data-warehousing. Tasks and algorithms of data mining. Classification methods including Decision trees (building, pruning, evaluation), Rule-based, Nearest Neighbor, Bayesian. Feature selection and Frequent item sets and association methods: Apriori, Compact Representation, FP trees. Clustering methods: k-means, Bisecting k-means, Agglomerative. The course also covers topics in Web-mining techniques and methods.
The course aims to provide students with advanced and in-depth understanding and analysis of the data mining methods and techniques in knowledge discovery, and apply these techniques through team project work and research investigation.