Author(s)

M. Spiliopoulou & J.F. Roddick

Abstract

To elate, most data mining algorithms and frameworks have concentrated on the extraction of interesting rules directly from collected data. This paper investigates the generic modelling of these rules and the utility of deriving rules from the results of other data mining routines, that is, mining from rulesets (or met a-mining). It is argued that this approach has three significant advantages. Firstly, with the expansion of clataset size, the tract ability of mining from the complete dataset may be difficult on a regular basis, secondly, changes in observations (and therefore in the observed system) can be more easily discovered by inspecting changes in extracted rules over time (or

Keywords