Matching data mining algorithm suitability to data characteristics using a self-organizing map

Abstract

The vast range of data mining algorithms available for learning classification problems has encouraged a trial-and-error approach to finding the best model. This problem is exacerbated by the fact that little is known about which techniques are suited to which types of problems. This paper provides some insights into the data characteristics that suit particular data mining algorithms. Our approach consists of four main stages. First, the performance of six leading data mining algorithms is examined across a collection of 57 well-known classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 57 problems in terms of data comp..