Providing access to expertise in the school of Computing Sciences.


Data Mining

Data mining is the automatic identification of patterns in large databases. These patterns represent potentially valuable and previously unknown knowledge hidden in the data.

Overview of Data Mining

In recent years, world-wide interest in Data Mining has soared. Organisations store vast amounts of information about their products, customers and many other areas of their business. The idea that these huge databases can be mined for interesting patterns has appealed to a wide range of organisations. Typical KDD projects may investigate customer behaviour, plan direct marketing, detect fraudulent activity, identify machine faults and many other objectives.

Data mining techniques used to automatically identify such patterns are drawn from a number of scientific disciplines, including statistics and machine learning. Scientists in the School of Computing Sciences at UEA have been specialising in the development of effective data mining techniques for over 20 years and have applied these in many application areas, mainly through commercially funded R&D projects managed through SYS Consulting.

How Businesses can benefit from Data Mining

Businesses in all sectors can benefit from data mining. Data collected and stored in data bases, including data archived in data warehouses, is data representing some real world process or processes. Patterns of behaviour which exist in the real world process will be captured with the data collected. The application of the appropriate techniques to identify and represent these patterns can lead to new knowledge about the data and hence the real world process.

Application areas include Medical/Health (Analysis of clinical treatment), Social Services (Analysing care data), Financial (Claim histories, Customer churn, Customer segmentation, Credit card fraud, Risk assessment), Industrial (Process control, Predictive Maintenance), Networks & Telecommunications (Mobile phone fraud, Intrusion detection through network flow data analysis, Mining 0n-line communities), Meteorological (Mid-term forecasts of average monthly rainfall and temperature) and many other sectors.

Case studies

See the panel to the left for links to Case Studies describing applications of various data mining techniques with client data. Examples are Visible Energy for carbon reduction, and managing train fault data for Alstom Transport.

We also include details of professional Data Mining Training, including our forthcoming 2 day KDD course, the annual UK KDD Symposium, UKKDD'09, sponsored by SYS Consulting, and a list of our Data Mining Clients.