Informatica

Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X² test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams; b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required.

Application of knowledge discovery in databases (data mining) for medical decision support is discussed in this work. The aim of the study was to use decision support algorithm for the differential diagnosis of intraocular tumors using parameters from eye images obtained by the ultrasound examination. Application of predictive modeling algorithm for decision tree formation using See5.0/C5.0 data mining system is presented. The decision tree was build using tumor geometry and microstructure parameters. The use of decision tree allows to differentiate tumors from other tumor-like formations. Low percentage of diagnostic errors shows that decision tree is reliable enough to offer it as “second opinion” for physician's decision support.