Cluster or co-cluster analyses are important tools in a variety of scientific areas. The
introduction of this book presents a state of the art of already well-established, as well as …

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means
partitioning being the most popular methods. Numerous improvements of these two …

Due to data sparseness and attribute redundancy in high-dimensional data, clusters of
objects often exist in subspaces rather than in the entire space. To effectively address this …

In this paper we present LeGo, a generic framework that utilizes existing local pattern mining
techniques for global modeling in a variety of diverse data mining tasks. In the spirit of well …

Due to the ability of graphs to represent more generic and more complicated relationships
among different objects, graph mining has played a significant role in data mining, attracting …

Conceptual clustering combines two long-standing machine learning tasks: the
unsupervised grou** of similar instances and their description by symbolic concepts. In …

In this position paper, we discuss how different branches of research on clustering and
pattern mining, while rather different at first glance, in fact have a lot in common and can …

Pattern mining is one of the most important aspects of data mining. By far the most popular
and well-known approach is frequent pattern mining. That is, to discover patterns that occur …

For a book, its title and abstract provide a good first impression of what to expect from it. For
a database, obtaining a good first impression is typically not so straightforward. While low …

In this paper, we discuss the multiple p-median problem (MPMP), an extension of the
original p-median problem and present several potential applications. The objective of the …