  Today, huge amounts of data are being collected with spatial and temporal
components from sources such as meteorological, satellite imagery etc.
Efficient visualisation as well as discovery of useful knowledge from these
datasets is therefore very challenging and becoming a massive economic need.
Data Mining has emerged as the technology to discover hidden knowledge in very
large amounts of data. Furthermore, data mining techniques could be applied to
decrease the large size of raw data by retrieving its useful knowledge as
representatives. As a consequence, instead of dealing with a large size of raw
data, we can use these representatives to visualise or to analyse without
losing important information. This paper presents a new approach based on
different clustering techniques for data reduction to help analyse very large
spatio-temporal data. We also present and discuss preliminary results of this
approach.
