A new immune algorithm for classification static and dynamic data

Abstract

In this paper we present a new algorithm for exploratory data analysis. It can be used for automated cluster extraction in static as well as dinamically changing data sets. The description of the algorithm is followed by a short overview of immune-based approaches to data analysis and machine learning. The entire algorithm is briefly described in Section 3. When coping with multidimensional data, problems with their visualization is presented; the algorithm reflects topological structure of extracted clusters rather than true data location in multidimensional space. Section 5 describes shortly numerical experiments with static and dinamically changing data sets. Section 6 colcludes the paper and Section 7 describes future developments.