FIT5142 - Advanced data mining - 2019

Advanced methods of discovering patterns in large-scale multi-dimensional databases are discussed. Solving classification, clustering, association rules analysis and regression problems on different kinds of data are covered. Data pre-processing methods for dealing with noisy and missing data in the context of Big Data are reviewed. Evaluation and analysis of data mining models are emphasised. Hands-on case studies in building data mining models are performed using popular modern software packages.

On successful completion of this unit, students should be able to:

1. explain the kinds of data from which knowledge can be mined, the way each data type can be presented to a data mining algorithm, the kinds of patterns that can be mined from each data type;
2. evaluate the quality of data mining models;
3. perform pre-processing of large-scale multi-dimensional data sets in preparation for data mining experiments;
4. perform data pre-processing for data with outliers, incomplete and noisy data;
5. compare the various learning algorithms and the ability to effectively apply suitable algorithms to mine frequent patterns and associations from data, to perform data classification, data clustering and regression analysis;
6. use modern data mining tools to solve non-trivial data mining problems;
7. research the current trends in data mining applications;
8. work in a team to extract knowledge from a common data set using various data mining methods and techniques.

Examination (2 hours): 60%; In-semester assessment: 40%

Minimum total expected workload equals 12 hours per week comprising:

1. Contact hours for on-campus students:
   • Two hours of lectures
   • One 2-hour laboratory
2. Additional requirements (all students):
   • A minimum of 8 hours independent study per week for completing lab and project work, private study and revision.

See also Unit timetable information