There is much recent discussion on data streams and big data, which except of their volume
and velocity are also characterized by volatility. Next to detecting change, it is also important …

Clustering data streams is a challenging problem that has received significant attention in
the recent decade. In this paper, we address the hitherto inadequately addressed challenge …

Data mining is concerned with detecting patterns of data in raw datasets, which are then
used to unearth knowledge that might not have been discovered using conventional …

This study optimises manually derived rule-based expert system classification of objects
according to changes in their properties over time. One of the key challenges that this study …

Classifying items using temporal data, ie several readings of the same attribute in different
time points, has many applications in the real world. The pivotal question which motivates …

Cluster tracing algorithms are used to mine temporal evolutions of clusters. Generally,
clusters represent groups of objects with similar values. In a temporal context like tracing …

In the US, many drinking water assets operate beyond their designed lifetimes, and
constrained resources necessitate optimizing repair and replacement. For pipe …

Les travaux de cette thèse s' intéressent à l'exploration de la multiplicité des solutions de
clustering. Le but est de proposer aux experts marketing un outil interactif d'exploration des …

Due to the rapid accumulation of temporal data in various fields like economy, medicine and
education (Deshpande & Karypis 2002; Wei & Keogh 2006; Lane & Brodley 1999; …

In the public goods game, players can be classified into different types according to their
participation in the game. It is an important issue for economists to be able to measure …