Quality Assessment Approaches in Data Mining

Abstract

The Datu Mining process encompasses many different specific techniques and algorithms that can be used to analyze the data and derive the discovered knowledge. An important problem regarding the results of the Data Mining process is the development of efficient indicators of assessing the quality of the results of the analysis. This, the quality assessment problem, is a cornerstone issue of the whole process because: i) The analyzed data may hide interesting patterns that the Data Mining methods are called to reveal. Due to the size of the data, the requirement for automatically evaluating the validity of the extracted patterns is stronger than ever. ii)A number of algorithms and techniques have been proposed which under different assumptions can lead to different results. iii)The number of patterns generated during the Data Mining process is very large but only a few of these patterns are likely to be of any interest to the domain expert who is analyzing the data. In this chapter we will introduce the main concepts and quality criteria in Data Mining. Also we will present an overview of approaches that have been proposed in the literature for evaluating the Data Mining results .