Simplified Progressive Data Mining

On-line data mining, concept hierarchy, frequent patterns, cover, obviosity

There are huge amounts of data stored in databases, but it is very difficult to
make decisions based on this data. We propose the OLAM SE system (Self Explaining
On-Line Analytical Mining) that is similar to the Han's OLAM [5] in the idea of
interactive data mining. The contribution is to simplify on-line analytical data
mining to professionals, who understand their data but want more significant,
interesting and useful information. It is done by shielding internal concepts
(associations, classifications, characterizations) and thresholds (supports,
confidences) from the user and by a simple graphical interface that suggests most
relevant items. OLAM SE determines minimum support value from required cover of
data with usage of entropy coding principle. This is automatically applied on the
structure based on given conceptual hierarchy where present. We also determine
the maximum threshold to avoid explaining knowledge that is obvious. Major part
of data is thus described by frequent patterns. The presentation of results is
realized using diagram notation similar to UML. In fact, it is a visual graph
which nodes are frequent data sets presented as packages including sub packages -
data concepts or items. Edges represent links or patterns between them. These
patterns can be progressively explored by the user, who gets a detailed view of
patterns which are attractive to him. Other possibly interesting sets are offered
to the user without any other action. This is well suitable for characterization
and descriptive classification equivalent to normal Bayes.