Interactivity Closes the GapLessons Learned in an Automotive Industry Application

Authors:
Axel Blumenstock;Markus Mueller;Carsten Lanquillon;Steffen Kempe;Jochen Hipp;Ruediger Wirth
Affiliations:
Quality Analysis, Daimler AG, Germany;University of Bamberg, Germany;Heilbronn University, Germany;Quality Analysis, Daimler AG, Germany;Quality Analysis, Daimler AG, Germany;Quality Analysis, Daimler AG, Germany
Venue:
Proceedings of the 2010 conference on Data Mining for Business Applications
Year:
2010

Citing 9
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Explora: a multipattern and multistrategy discovery assistant

Advances in knowledge discovery and data mining
Multi-level organization and summarization of the discovered rules

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm for Multi-relational Discovery of Subgroups

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Applications and Research Problems of Subgroup Mining

ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Summarizing itemset patterns: a profile-based approach

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Opportunity map: identifying causes of failure - a deployed data mining system

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Rule cubes for causal investigations

Knowledge and Information Systems
Knowledge-Based sampling for subgroup discovery

LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection

Quantified Score

Hi-index	0.00

Visualization

Abstract

After nearly two decades of data mining research there are many commercial mining tools available, and a wide range of algorithms can be found in literature. One might think there is a solution to most of the problems practitioners face. In our application of descriptive induction on warranty data, however, we found a considerable gap between many standard solutions and our practical needs. Confronted with challenging data and requirements such as understandability and support of existing work flows, we tried many things that did not work, ending up in simple solutions that do. We feel that the problems we faced are not so uncommon, and would like to advocate that it is better to focus on simplicity---allowing domain experts to bring in their knowledge---rather than on complex algorithms. Interactivity and simplicity turn out to be key features to success.