In this paper we would like to suggest a modification of data vases visualization that can help understanding complex data where each subject is described by a vector of synchronized signals taken in the same time intervals. The advantages of our new approach were verified when visualizing set of Brdicka curves and answering an interesting question raised in proteomics about the Brdicka curves proportionality.

RadViz visualization makes it possible to map data from n-dimensional space into a plane. The paper reviews those specific properties of this method that are important for identification of clusters in the original multidimensional data. First, there is described an artificial data set which clearly points to a certain drawback of the original RadViz mapping. To resolve the identified problem there are suggested 2 minor modifications of the RadViz algorithm. Finally, it is proved that application of both suggested modifications guarantees that the upper mentioned problem does not re-appear. This claim is experimentally confirmed by a new visualization of the two original data sets using the modified mapping algorithm.

Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches based, respectively, on windowing, episode rules, and inductive logic programming. Windowing is one of the essential methods of data preprocessing. Episode rules represent general sequential mining, while inductive logic programming extracts first-order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of a series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential/temporal patterns. Possible relations between the patterns and an onset of any of the observed cardiovascular diseases are also studied.