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Alexandre Vautier and Marie-Odile Cordier and René Quiniou
Towards data mining without information on knowledge structure
, PKDD'07 (Principles and Practice of Knowledge Discovery in Databases)
, Warsaw
, Vol. 4702
, 300-311
, sep
, 2007
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Abstract
Most knowledge discovery processes are biased since some part of the knowledge
structure must be given before extraction. We propose a framework that avoids
this bias by supporting all major model structures e.g. clustering, sequences,
etc., as well as specifications of data and DM (Data Mining) algorithms, in the
same language. A unification operation is provided to match automatically the
data to the relevant DM algorithms in order to extract models and their related
structure. The MDL principle is used to evaluate and rank models. This
evaluation is based on the covering relation that links the data to the models.
The notion of schema, related to the category theory, is
the key concept of our approach. Intuitively, a schema is an algebraic
specification enhanced by the union of types, and the concepts of list and
relation. An example based on network alarm mining illustrates the process.
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