and co-requisite modules: CO532 Database Systems
OverviewData mining is a process of extracting, from a large amount of data, interesting patterns that are non-trivial, hidden, new and potentially useful. It is a rapidly growing field and is becoming important because with the increasing quantity and variety of online data collections by many organizations and commercial enterprises, there is a high potential value of patterns discovered in those collections.
This module looks at different data mining techniques and gives you the chance to use a state-of-the-art data-mining tool and evaluate the quality of the discovered knowledge. The topics include: introduction to data mining and knowledge discovery process, data description, data warehousing and OLAP, data pre-processing, overview of basic data mining tasks, market basket analysis and association rules, classification using decision tree induction, Naïve Bayesian classification, K-means clustering, outlier detection, post-processing, social impact and trend of data mining.
This module appears in:
22 hours of lectures , 11 hours of classes
understand the motivation for data mining in the context of business and information technology
know how data mining is used, particularly for marketing, sales and customer relationship management
understand the concepts and main techniques in data mining
be able to describe the differences between the major data mining tasks
have an understanding of the knowledge discovery process
understand the purpose of the main tasks involved in data preparation for mining
gain hands-on experience in using a state-of-the-art data mining tool