Intuitive and Interactive Mining

dolování dat, OLAP, OLAM, interaktivní, intuitivní, podpora, pokrytí, zřejmost.

In this paper, we propose a framework, internally called OLAM SE, for interactive and intuitive mining of multilevel association, characterization and classification rules. This framework is proposed as an extension of OLAP or an alternative to Han's OLAM. OLAM processes data stored in data cubes structure of which is based on a given conceptual hierarchy. OLAM SE determines minimum support value from user defined cover of data with usage of entropy coding principle. It also automatically determines the maximum threshold
to avoid explaining obvious. Major part of data is thus described by frequent patterns. The presentation of results is inspired by UML diagram notation. It is a graph nodes of which are frequent data sets represented as packages including sub packages - data classes or items. Edges represent relations or patterns
between packages. These patterns can be interactively explored by the user, who gets a detailed view of attractive ones. Other, possibly interesting, sets are intuitively offered to her. This is well applicable for the characterization and non-naive Bayesian classification.