Manish Mehta

Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.

The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of data-intensive decision-support applications. This paper is a capsule summary of the current

This paper explores the application of the Minimum Description Length principle for pruning decision trees. We present a new algorithm that intuitively captures the primary goal of reducing the misclassification error. An experimental comparison is presented with three other pruning algorithms. The results show that the MDL pruning algorithm achieves good accuracy, small trees, and fast execution times. Introduction Construction or "induction" of decision trees from examples has been the subject of extensive research in the past [Breiman et. al. 84, Quinlan 86]. It is typically performed in two steps. First, training data is used to grow a decision tree. Then in the second step, called pruning, the tree is reduced to prevent "overfitting". There are two broad classes of pruning algorithms. The first class includes algorithms like cost-complexity pruning [Breiman et. al., 84], that use a separate set of samples for pruning, distinct from the set used to grow the tree. In many cases, ...

In this paper we present the results of a qualitative, empirical study exploring the impact of immersive technologies on presence and engagement, using the interactive drama Façade as the object of study. In this drama, players are situated in a married couple's apartment, and interact primarily through conversation with the characters and manipulation of objects in the space. We present participants' experiences across three different versions of Façade -- augmented reality (AR) and two desktop computing based implementations, one where players communicate using speech and the other using typed keyboard input. Through interviews and observations of players, we find that immersive AR can create an increased sense of presence, confirming generally held expectations. However, we demonstrate that increased presence does not necessarily lead to more engagement. Rather, mediation may be necessary for some players to fully engage with certain interactive media experiences.