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New trends and applications in data mining

New trends and applications in data mining
New trends and applications in data mining
Over the past few years, data mining has grown from a relatively unknown discipline into a widespread billion dollar business. Being first only adopted in the retail and banking sectors, we can nowadays observe a proliferation of the application domains, like for instance in e-commerce, terrorism prevention, RFID, software engineering, pharmaceutics, and bio-informatics. In this presentation, we cover some of these recent application domains and explain how data mining can contribute towards an increased efficiency in these fields. In the second part, we present some new data mining techniques that are expected to make a rapid transition into business environments. Techniques that will be discussed are, amongst others, support vector machines (SVMs), Bayesian network classifiers, bagging and boosting, and multirelational data mining.
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Baesens, Bart (2006) New trends and applications in data mining. 14th Annual Meeting of the Belgian Statistical Society. 11 - 13 Oct 2006.

Record type: Conference or Workshop Item (Other)

Abstract

Over the past few years, data mining has grown from a relatively unknown discipline into a widespread billion dollar business. Being first only adopted in the retail and banking sectors, we can nowadays observe a proliferation of the application domains, like for instance in e-commerce, terrorism prevention, RFID, software engineering, pharmaceutics, and bio-informatics. In this presentation, we cover some of these recent application domains and explain how data mining can contribute towards an increased efficiency in these fields. In the second part, we present some new data mining techniques that are expected to make a rapid transition into business environments. Techniques that will be discussed are, amongst others, support vector machines (SVMs), Bayesian network classifiers, bagging and boosting, and multirelational data mining.

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More information

Published date: 2006
Venue - Dates: 14th Annual Meeting of the Belgian Statistical Society, 2006-10-11 - 2006-10-13

Identifiers

Local EPrints ID: 42656
URI: https://eprints.soton.ac.uk/id/eprint/42656
PURE UUID: 094307a6-3547-45a3-9924-c699f68ee079

Catalogue record

Date deposited: 22 Jan 2007
Last modified: 13 Mar 2019 21:11

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