{24 () Parallel Formulations of Decision-tree Classiication Algorithms

Classiication decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classiication decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classiication decision trees have a natural concurrency, but are diicult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classiication decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.