Reclassification Rules
Li-Shiang Tsay, Zbigniew W. Raś, Seunghyun Im · 2008
The ultimate goal of knowledge discovery (KD) is to extract sets of patterns leading to useful knowledge for obtaining user desirable outcomes. The key characteristics of knowledge usefulness is that these patterns are actionable. In the last decade, KD algorithms such as mining for association rules, clustering, and classification rules, have made a tremendous progress and have been demonstrated to be of significant value in a variety of real-world data mining applications. However, the results of the existing methods require to be further processed in order to suggest actions that achieve the desired outcome, by giving only previously acquired data. To address this issue, we present a novel technique, called reclassification rules, to gather all facts, to understand their causes and effects, and to list all potential solutions and the responding effects. Algorithm, Strategy Generator-II, is proposed to discover a complete set of reclassification rules which meets pre-specified constraints.