Data Mining in Temporal Databases

Abstract

In this paper we describe our approach to data mining in temporal databases by introducing Easy Miner, a data mining system developed at UMIST. This system implements a wide spectrum of data mining functions, including generalisation, relevance analysis, classification and discovery of association rules. By integrating these interesting data mining techniques, the system provides a user friendly and interactive environment with good performance and of course, wide choice of functionalities. These algorithms have been tested on time-oriented medical data and experimental results show that the algorithms are efficient and effective for discovery of previously unknown knowledge in databases. 1. Introduction Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data [1]. In other words, it is the search for relationships and global patterns that exist in large databases, but are "hidden" among the vast amount of data, such as a r..