Data mining on the installed base information: Possibilities and implementations

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Authors: Bakirov, R. and Stich, C.

Editors: Filipe, J. and Fred, A.L.N.

http://www.informatik.uni-trier.de/~ley/db/conf/icaart/icaart2011-1.html

Journal: ICAART (1)

Pages: 649-654

Publisher: SciTePress

ISBN: 978-989-8425-40-9

This source preferred by Rashid Bakirov

This data was imported from Scopus:

Authors: Bakirov, R. and Stich, C.

Journal: ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

Volume: 1

Pages: 649-654

ISBN: 9789898425409

Managing the installed base at customer sites is a key for customer satisfaction. Hereby installed base comprises installed systems and products at customer sites which are currently being serviced by the producer company. The purpose of the present study is developing use cases for data mining on the installed base information of a large manufacturing company and specifically ABB, and constructing data mining models for their implementation. The aim is to use the available information to enhance customer-tailored sales and proactive service. This includes recommendations to customers and failure prediction. The developed models employ association rules mining, classification and regression, realized with the help of data mining tools Oracle Data Mining and Weka. Results have been evaluated using statistical means, as well as discussed with the experts at the company. These results suggest that with the reasonable amount of data, installed base information is a potential source for data mining models useful for business intelligence.

This data was imported from Web of Science (Lite):

Authors: Bakirov, R. and Stich, C.

Journal: ICAART 2011: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1

Pages: 649-654

ISBN: 978-989-8425-40-9

The data on this page was last updated at 05:09 on February 27, 2020.