With the continued digitization of societal processes, we are seeing an explosion in
available data. This is referred to as big data. In a research setting, three aspects of the data …

Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

Abstract Art often provides valuable hints for technological innovations especially in the field
of Image Processing and Computer Graphics. In this paper we survey in a unified framework …

A time series is “a sequence X=(X1, X2, xm) of observed data over time", where m is the
number of observations. Tracking the behavior of a specific phenomenon/data in time can …

The aim of this book is to introduce the reader to some of the best techniques for data mining
in bioinformatics in the hope that the reader will build on them to make new discoveries on …

This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete
parts, stars, cliques and paths into supernodes. The supernodes carry a synopsis S_Q for …

We review the major paradigms for approximate similarity queries and propose a
classification schema that easily allows existing approaches to be compared along several …

Local classifiers are sometimes called lazy learners because they do not train a classifier
until presented with a test sample. However, such methods are generally not completely lazy …

This chapter describes several methods of similarity search, based on metric indexing, in
terms of their common, underlying principles. Several approaches to creating lower bounds …

Text document clustering provides an effective and intuitive navigation mechanism to
organize a large amount of retrieval results by grou** documents in a small number of …