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

Reuben Evans

ISBN 10: 3639031636 / ISBN 13: 9783639031638

Published by VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008

About this Item

Neuware - 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.

Seller Inventory # 9783639031638

Bibliographic Details

• Title: Clustering for Classification : Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classification Accuracy
• Publisher: VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K.
• Publication Date: 2008
• Language: English
• ISBN 10: 3639031636
• ISBN 13: 9783639031638
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 179 grams

About this title

Synopsis

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.