Collaborative Research: Data Mining: Theory and Algorithms

这个合作研究项目是由伊利诺伊大学的伦纳德皮特和H。V. Jagadish在密歇根大学的研究，借鉴了Pitt教授在计算学习理论方面的经验，以及Jagadish教授在数据库方面的专业知识。本研究的目标是开发一个理论框架来研究数据挖掘问题，并适应现有的机器学习算法，并在框架内开发新的算法。通常，机器学习算法的重点是学习适用于大多数（标记）数据的单个分类器，而数据挖掘则侧重于学习“启发式”规则，通常是未标记的数据，这些规则可以深入了解数据的性质。通过统一的技术从前者的领域与后者的目标，新的聚类算法的开发，通过采样处理大量的数据集，但提供最优性保证。该研究还提供了标准，数据挖掘从业者可以应用在决定是否聚合或采样是一个首选的数据减少技术的任务。新的和有用的类型的数据模式的算法进行了设计，并将用户的数据挖掘任务的算法进行了分析和开发的框架内。

This collaborative research project is carried out by Leonard Pitt at the University of Illinois and H. V. Jagadish at the University of Michigan, drawing on Professor Pitt's experience in computational learning theory, and Professor Jagadish's expertise in databases. The goal of this research is to develop a theoretical framework for investigating data mining problems, and to adapt existing machine learning algorithms and develop new ones within the framework. Typically, the focus of Machine Learning algorithms is on learning a single classifier that works well for most of the (labeled) data, whereas data mining focuses on learning "heuristic" rules, typically on unlabeled data, that give insight into the nature of the data. By unifying techniques from the former area with goals of the latter, new clustering algorithms are developed that deal with massive data sets via sampling, yet provide optimality guarantees. The research also provides criteria that data mining practitioners may apply in deciding whether aggregation or sampling is a preferred data reduction technique for the task at hand. Algorithms for new and useful types of data patterns are designed, and algorithms that incorporate the user into the data-mining task are analyzed and developed within the framework.