Partition Incremental Discretization

In this paper we propose a new method to perform incremental discretization. This approach consists in splitting the task in two layers. The first layer receives the sequence of input data and stores statistics of this data, using a higher number of intervals than what is usually required. The final discretization is generated by the second layer, based on the statistics stored by the previous layer. The proposed architecture processes streaming examples in a single scan, in constant time and space even for infinite sequences of examples. We demonstrate with examples that incremental discretization achieves better results than batch discretization, maintaining the performance of learning algorithms. The proposed method is much more appropriate to evaluate incremental algorithms, and in problems where data flows continuously as most of recent data mining applications