Data Mining Techniques in Processing Medical Knowledge

Data mining is an evolving and growing area of research and development, both in academia as well as in industry. It involves interdisciplinary research and development encompassing diverse domains. In this age of multimedia data exploration, data mining should no longer be restricted to the mining of knowledge from large volumes of high-dimensional data sets in traditional databases only. The aim of the paper is to develop a new learning by examples PCA-based algorithm for extracting skeleton information from data to assure both good recognition performances, and generalization capabilities in case of large data set. The classes are represented in the measurement/feature space by continuous repartitions, that is the model is given by the family of density functions ( ) H h h f ∈ , where H stands for the finite set of hypothesis (classes). The basis of the learning process is represented by samples of possible different sizes coming from the considered classes. The skeleton of each class is given by the principal components obtained for the corresponding sample.