Postgraduate - UnitFIT5045 - Knowledge discovery and data mining

At the completion of this unit students will:

• be able to differentiate between supervised and unsupervised learning;
• know how to apply the main techniques for supervised and unsupervised learning;
• know how to use statistical methods for evaluating data mining models;
• be able to perform data pre-processing for data with outliers, incomplete and noisy data;
• be able to extract and analyse patterns from data using a data mining tool;
• have an understanding of the difference between discovery of hidden patterns and simple query extractions in a dataset;
• have an understanding of the different methods available to facilitate discovery of hidden patterns in a dataset;
• have developed the ability to preprocess data in preparation for data mining experiments;
• have developed the ability to evaluate the quality of data mining models;
• be able to appreciate the need to have representative sample input data to enable learning of patterns embedded in population data;
• be able to appreciate the need to provide quality input data to produce useful data mining models;
• have acquired the skill to use the common features in data mining tools;
• have acquired the skill to use the visualisation features in a data mining tools to facilitate knowledge discovery from a data set;
• have acquired the skill to compare data mining models based on the results on a set of performance criteria;
• be able to work in a team to extract knowledge from a common data set using different data mining methods and techniques.