Clustering for Classification: Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classification Accuracy

Evans, Reuben

ISBN 10: 3639031636 / ISBN 13: 9783639031638

Editore: VDM Verlag, 2008

Riguardo questo articolo

LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS.

Codice articolo ERICA79036390316366

Dati bibliografici

• Titolo: Clustering for Classification: Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classification Accuracy
• Editore: VDM Verlag
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3639031636
• ISBN 13: 9783639031638
• Rilegatura: Paperback
• Condizione: Like New
• Tipo di libro: book

Descrizione articolo

Riassunto

Advances in technology have provided industry with an array of de­vices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadra­tic in the number of data points in fact, and so cannot be applied directly. This book proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still 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.