Clustering for Classification
Evans, R. J. E. (2007). Clustering for Classification (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from http://hdl.handle.net/10289/2403
Permanent Research Commons link: http://hdl.handle.net/10289/2403
Advances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means thatthere are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods ofclassification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, so cannot be applied directly. This thesis proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller butstill representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.
The University of Waikato
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