Abstract The field of Large-Scale Metrology has been studied extensively for many decades
and represents the combination and competition of topics as diverse as geodesy and …

The development of information technology has enabled many different technologies to
collect large amounts of spatial data every day. It is of very great significance to discover …

We propose Coactive Learning as a model of interaction between a learning system and a
human user, where both have the common goal of providing results of maximum utility to the …

Experts in racket sports like tennis and badminton use tactical analysis to gain insight into
competitors' playing styles. Many data-driven methods apply pattern mining to racket sports …

We live in the era of data and need tools to discover valuable information in large amounts
of data. The goal of exploratory data mining is to provide as much insight in given data as …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Label ranking is a machine learning task that deals with map** an instance to a ranking of
labels, representing the labels' ordered relevance to the instance. Three recent studies have …

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …

Co-location pattern mining plays an important role in spatial data mining. With the rapid
growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the …

The utility of a dense subgraph in gaining a better understanding of a graph has been
formalised in numerous ways, each striking a different balance between approximating …