Today's Internet is a massive, distributed network which continues to explode in size as e-
commerce and related activities grow. The heterogeneous and largely unregulated structure …

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …

Introduction to Information Retrieval is the first textbook with a coherent treatment of classical
and web information retrieval, including web search and the related areas of text …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

The modeling and analysis of computer communications networks give rise to a variety of
interesting statistical problems. This chapter focuses on network tomography, a term used to …

Inferring the routing topology and link performance from a node to a set of other nodes is an
important component in network monitoring and application design. In this paper, we …

Supervised learning is a classic data mining problem where one wishes to be be able to
predict an output value associated with a particular input vector. We present a new twist on …

Hiererachical clustering, that is computing a recursive partitioning of a dataset to obtain
clusters at increasingly finer granularity is a fundamental problem in data analysis. Although …

An emerging body of findings indicates that hierarchy is critical to integrating the Big Trait
models–the Big Five, Big Four, Big Three, and Big Two–within a common structural …