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Data mining challenges in the automotive domain

Published: 20 August 2006 Publication History

Abstract

Automotive companies, such as Ford Motor Company, have no shortage of large databases with abundant opportunities for cost reduction and revenue enhancement. The Data Mining Group at Ford has worked in the areas of Quality, Customer Satisfaction and Warranty Analytics for close to ten years. In this time, we have developed a number of methods for building systems to help the business. One area of particular success has been in warranty analysis. While traditional hazard analysis has been applied at Ford for a number of years, we have used techniques from other industries (e.g. retail), as well as text mining to view warranty analytics in a new way. However, our success has been tempered by serious challenges particularly in the areas of data understanding, computing meaningful aggregations and implementation. Case studies from the automobile industry (warranty, quality, forecasting, etc.) as well as from other industries will be used.

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cover image ACM Conferences
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2006
986 pages
ISBN:1595933395
DOI:10.1145/1150402
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 20 August 2006

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