互动性弥合差距——汽车工业应用的经验教训

A. Blumenstock, Markus Mueller, Carsten Lanquillon, S. Kempe, Jochen Hipp, R. Wirth
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引用次数: 7

摘要

经过近二十年的数据挖掘研究,有许多商业挖掘工具可用,并且可以在文献中找到各种各样的算法。有人可能认为从业者面临的大多数问题都有一个解决方案。然而,在我们对保修数据进行描述性归纳的应用中,我们发现许多标准解决方案与我们的实际需求存在相当大的差距。面对具有挑战性的数据和需求,例如现有工作流的可理解性和支持,我们尝试了许多不起作用的事情,最终得到了简单的解决方案。我们觉得我们面临的问题并不是那么罕见,并且希望提倡将重点放在简单性上——允许领域专家引入他们的知识——而不是复杂的算法上。互动性和简单性是成功的关键。
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Interactivity Closes the Gap - Lessons Learned in an Automotive Industry Application
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.
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