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From Computer Data to Human Knowledge: A Cognitive Approach to Knowlege Discovery and Data Mining

从计算机数据到人类知识：知识发现和数据挖掘的认知方法

基本信息

批准号：
9731990
负责人：
Michael Pazzani
金额：
$ 32万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1998
资助国家：
美国
起止时间：
1998-10-01 至 2001-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9731990&HistoricalAwards=false
关键词：
Computer Data Human Knowledge Cognitive

项目摘要

This research is being done in conjunction with IIS-9732562, which is beingperformed by Dorrit Billman in the Psychology Department at GeorgiaInstitute of Technology. The research is concerned with intelligentdecision aids that can be developed by data mining techniques. Experiencehas determined that such systems can learn accurate models, but thatexperts in areas where those models are used in decision aids are oftenreluctant to trust them because they do not, for instance, use the sametests intermediate conclusions or abstractions that the experts have grownto trust. Or they do not use certain factors at all that experts feel tobe relevant. Experts also want models that are stable under small changesin the data being analyzed. Psychologists have discovered factors thatsimplify the learning, understanding, and communication of category andprocess information by humans. This research seeks to explore thesepsychological principles in light of the output of existing KDD algorithmsand then go on to develop and evaluate new KDD algorithms that will provideoutput that is easy for people to learn , use, and communicate to others.With the results of this research, it should be possible to make suchdecision aids more "human centered", so that they will be used more oftenand more effectively in practice.http://www.ics.uci.edu/~pazzani

这项研究是与IIS-9732562一起进行的，由乔治亚理工学院心理学系的Dorrit Billman进行。该研究关注的是可以通过数据挖掘技术开发的智能决策辅助工具。经验已经确定，这样的系统可以学习准确的模型，但是在那些模型用于决策辅助的领域，专家们通常不愿意信任它们，因为他们不使用相同的测试，例如，专家们已经成长为信任的中间结论或抽象。或者他们根本不使用专家认为相关的某些因素。专家们还希望模型在被分析数据的微小变化下保持稳定。心理学家已经发现了简化人类学习、理解和交流类别和过程信息的因素。本研究试图根据现有KDD算法的输出结果来探索这些心理学原理，并进而开发和评估新的KDD算法，以提供易于学习、使用和交流的输出结果，有了本研究的结果，应该有可能使这种决策辅助工具更加“以人为本”，从而使它们在practice.http://www.ics.uci.edu/~pazzani中得到更频繁、更有效的使用