A scalable decision tree system and its application in pattern recognition and intrusion detection

Abstract

One of the most challenging problems in data mining is to develop scalable algorithms capable of mining massive data sets whose sizes exceed the capacity of a computer's memory. In this paper, we propose a new decision tree algorithm, named SURPASS (for Scaling Up Recursive Partitioning with Sufficient Statistics), that is highly effective in handling such large data. SURPASS incorporates linear discriminants into decision trees' recursive partitioning process. In SURPASS, the information required to build a decision tree is summarized into a set of sufficient statistics, which can be gathered incrementally from the data, by reading a subset of the data from storage space to main memory one at a time. As a result, the data size that can be handled by this algorithm is independent of memory size. We apply SURPASS to three large data sets pertaining to pattern recognition and intrusion detection problems. The results indicate that SURPASS scales up well against large data sets and produces decision tree models with very high quality.