Scalable mining for classification rules in relational databases

Abstract

Classification is a key function of many business intelligence toolkits and a fundamental building block in data mining. Immense data may be needed to train a classifier for good accuracy. The state-of-art classifiers need an in-memory data structure of size O(N), where N is the size of the training data, to achieve efficiency. For large data sets, such a data structure will not fit in the internal memory. The best previously known classifier does a quadratic number of I/Os for large N. We propose a novel classification algorithm (classifier) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classifier to be built into a relational database system directly. MIND is truly scalable with respect to I/O efficiency, which is important since scalability is a key requirement for any data mining algorithm. We built a prototype of MIND in the relational database manager DB2 and benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of the datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond the dataset sizes previously published for classifiers. We also give some insights that may have an impact on the evolution of the extended relational calculus SQL.