IBaI Institute |
9th Industrial Conference on Data Mining ICDM´2009
July 20 - 22, 2009, Leipzig/Germany
Distances in Classification
Prof. Dr. Claus Weihs
Department of Statistics
The notion of distance is the most important basis for classification.
This is especially true for unsupervised learning, i.e. clustering, since
there is no validation mechanism by means of objects with of known groups.
For every individual problem the adequate distance has to be decided upon.
This is demonstrated by means of three practical examples from very
different application areas, namely social science, music science, and
In social science often models are used which take spatial distances
between objects into account which might have very irregular borders.
These borders have to be taken into account when defining distances.
In music science the main problem is often to find an adequate
transformation of the input time series as the basis for distance
definition. Also, local modelling is proposed in order to account for
different subpopulations, e.g. instruments.
In production economics often many quality criteria have to taken into
account with very different scaling. In order to find a compromise
optimum classification, this leads to a pre-transformation onto the
same scale, called desirability.
Predictive Targeting: Buzzword or Reality
The potential of automatic behavioral targeted advertising
in Online Marketing.
CEO Antz21 GmbH
This talk gives an overview on the state of art in behavioral
targeting automation. It shows the business power if you
manage to do it right and focuses as well on the related
technical and statistical problems:
- using the realtime searching patters,
- dirty mass data,
- changing contents on sites and bad tacking,
- very less datasets to learn on.