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Data Mining and Visualisation in High Dimensional Spaces


Department of Computer Science

Applications accepted all year round Self-Funded PhD Students Only

About the Project

The mining and the visualisation of data in high dimensional feature spaces require the design of efficient algorithms. In high dimensional data spaces distance functions lose their usefulness and optimisation techniques, machine learning and data mining algorithms are very inefficient and ineffective. This problem is referred to as ’the curse of dimensionality’ and is caused by the exponential increase in volume associated with adding extra dimensions to a mathematical space. In general, dimensionality reduction is a fundamental methodology for the success of the knowledge discovery process in many real-world applications. For enquiries please contact . Keywords: Data Mining, Data Visualisation, Dimensionality Reduction

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