Mining sequential data is an old topic that has been revived in the last decade, due to the
increasing availability of sequential datasets. Most works in this field are centred on the …

Satellite Image Time Series are becoming increasingly available and will continue to do so
in the coming years thanks to the launch of space missions which aim at providing a …

Clustering is one type of unsupervised learning where the goal is to partition the set of objects
into groups called clusters. Faced to the difficulty to design a general purpose clustering …

Summarizing a set of sequences is an old topic that has been revived in the last decade, due
to the increasing availability of sequential datasets. The definition of a consensus object is …

The increasing availability of High Spatial Resolution (HSR) satellite images is an opportunity
to characterize and identify urban objects. Thus, the augmentation of the precision led to a …

The multiplication of Very High Resolution (spatial or spectral) remote sensing images appears
to be an opportu- nity to identify objects in urban and periurban areas. The classification …

Although agreement between the annotators who mark feature locations within images has
been studied in the past from a statistical viewpoint, little work has attempted to quantify the …

The aim of collaborative clustering is to make different clustering methods collaborate, in
order to reach at an agreement on the partitioning of a common dataset. As different clustering …

Satellite image time series (SITS) analysis is an important domain with various applications
in land study. In the coming years, both high temporal and high spatial resolution SITS will …

Time series are ubiquitous in data mining applications. Similar to other types of data, annotations
can be challenging to acquire, thus preventing from training time series classification …