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Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algo rithms have been devel- oped such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the in- creasing size of databases, many existing rule learning alg orithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have bee n introduced. As TDIDT is the most popular classifier, even though there are strongl y competitive alterna- tive algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction