Scalable classifiers with dynamic pruning

This paper presents an algorithm to solve the problem of classification for data mining applications. This is a decision tree classifier which uses modified gini index as the partitioning criteria. Pre-sorting technique is used to overcome the problem of sorting at each node of the tree. This technique is integrated with a breadth first tree growth strategy which enables us to calculate the best partition for each of the leaf node in a single scan of database. We have implemented this algorithm using depth first tree growth strategy also. The algorithm uses Dynamic Pruning approach which reduces the number of scans of the database and does away with a separate tree pruning phase. The proof of correctness, analysis and performance study are also presented.