Automated classification algorithms for high-dimensional data

This thesis deals with the mathematical problem of automated classification. The goal is to identify classes in a data set on which no a priori knowledge is available.;When a priori information about the existing classes in the data is available, algorithms using learning samples are an interesting alternative to automated classification procedures. Two algorithms that classify data by the use of learning samples are presented and their performances are compared to the previous test results.;Further, the effect of using different distance measures on the automatic classification algorithms is discussed. These alternative distance functions allow the classification algorithms to overlook trivial differences in the data objects like constant offsets or uniform shifts.;The work focuses on the evaluation of five automated classification algorithms. Their performances are analyzed on specifically designed data sets. The results for these algorithms are benchmarked against each other, strengths and weaknesses are explained.