The course will introduce principles and techniques of data-mining and knowledge-based decision support for E-Commerce. Topics will include clustering, regression, classification, estimation, forecasting, statistical analysis, warehousing principles, knowledge acquisition and representation, as well as decision support systems.

CILO-1: Apply data pre-processing and generalization techniques to provide suitable input for a range of data mining algorithms. CILO-2: Access and compare different data mining models in supervised learning, unsupervised learning, time-series-forecasting, association, outlier detection and texting mining. CILO-3: Apply classification, clustering, association rule learning and regression techniques for data-decision support systems. CILO-4: Assess and compare the steps involved in the data mining process to build up decision support systems. CILO-5: Communicate effectively through clear and concise language, using KDD reporting style and presentation of concise executive summary, backed by empirical data and analysis. CILO-6: Evaluate the performance and accuracy of the main tasks of data mining models: description, estimation, prediction, classification, clustering, and association.