| Summary: | The development of information technology allows organisations to gather and store ever increasing quantities of data. This data, although not often collected specifically for such a purpose, may be processed to extract knowledge which is interesting, novel and useful. Such processing, known as data mining, demands algorithms which can efficiently 'mine' through the large volumes of data and extract patterns of interest. Modern heuristic techniques are a class of optimisation algorithms, which solve problems by searching through the space containing all possible solutions. They have been applied to a wide variety of such problems with great success, which suggests that they may also prove useful for data mining. Conducting a search through the space of all patterns within a database using such techniques is likely to yield useful information. Within this thesis, it is demonstrated that modern heuristic techniques may be successfully applied to a wide range of data mining problems. The results presented highlight the suitability of such algorithms for the demands of the commercial environment; as a consequence of this, much of the work undertaken has become incorporated within real business processes, bringing considerable savings. A variety of algorithmic enhancements are also investigated, yielding important results for both the data mining and heuristics fields.
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