Clustering for classification

About this item

Title

Clustering for classification

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

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, so cannot be applied directly. This paper proposes a ...

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Format

Research paper

Research format

Conference item

Date created

2011

Creator

Evans, Reuben James Emmanuel / Pfahringer, Bernhard / Holmes, Geoffrey

URL

https://hdl.handle.net/10289/6004

Related subjects

computer science / classification methods / clustering methods / random selection / support vector machines / Machine learning

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