In this paper we describe learning algorithms for Web page rank prediction. We consider linear regression models and combinations of regression with probabilistic clustering and Principal Components Analysis (PCA). These models are learned from time-series data sets and can predict the ranking of a set of Web pages in some future time. The first algorithm uses separate linear regression models....

Clustering is a basic task in a variety of machine learning applications. Partitioning a set of input vectors into compact, wellseparated subsets can be severely affected by the presence of modelincompatible inputs called outliers. The present paper develops robust clustering algorithms for jointly partitioning the data and identifying the outliers. The novel approach relies on translating scar...

The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental nonnative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to ...

Clustering is a basic and useful method in understanding and exploring a data set. Clustering is division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Interest in clustering has increased recently in new areas of applications including data mining, bioinformatics, web mining...

We present a novel single facility due-date quotation model and algorithm, and characterize the effectiveness of this algorithm employing the tools of probabilistic analysis. This model is intended to be a building block for the models that we will use to analyze due date quotation in make-to-order supply chains, and we present a summary of these models.