DATA MINING IN TEMPORAL DATABASES

In this paper we describe our approaches to data mining in te mporal databases by introducing Easy Miner, our data mining system developed at UMIST. Easy Miner integrates machine learning methodologies with database technologies and efficiently and effectively extract inte resting rules from data. The discovery components of E asy Miner, relevance analysis, classification and associ ation rules finder are presented with the algorithms behind each component. We also show the effectiveness of these a lgorithms from the experimental results obtained of applying these techniques to time-oriented medical data.