Abstract

Various tools and systems for knowledge discovery and data mining are developed and available for applications. However, when we are immersed in heaps of databases, an immediate question facing practitioners is where we should start mining. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most likely relevant to an application; without doing so, the mining process can be lengthy, aimless and ineffective. A relevance measure is thus proposed to identify relevant databases for mining tasks with an objective to find patterns or regularities about certain attributes. An efficient implementation for identifying relevant databases is described. Experiments are conducted to validate the measure's performance and to show its promising applications.

Citations