Informatica

Background: In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet.

Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-labeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.

In a supervised learning, the relationship between the available data and the performance (what is learnt) is not well understood. How much data to use, or when to stop the learning process, are the key questions.

In this article, a method is proposed for analysing the thermovision-based video data that characterize the dynamics of temperature anisotropy of the heart tissue in a spatial domain. Many cardiac rhythm disturbances at present time are treated by applying destructive energy sources. One of the most common source and the related methodology is to use radio-frequency ablation procedure. However, the rate of the risk of complications including arrhythmia recurrence remains enough high. The drawback of the methodology used is that the suchlike destruction procedure cannot be monitored by visual spectra and results in the inability to control the ablation efficiency. To the end of understanding the nature of possible complications and controlling the treating process, the means of thermovision could be used. The aim of the study was to analyse possible mechanisms of these complications, measure and determine optimal radio-frequency ablation parameters, according to the analysis of video data, acquired using thermovision.

Recent changes in the intersection of the fields of intelligent systems optimization and statistical learning are surveyed. These changes bring new theoretical and computational challenges to the existing research areas racing from web page mining to computer vision, pattern recognition, financial mathematics, bioinformatics and many other ones.

We tested the ability of humans and machines (data mining techniques) to assign stress to Slovene words. This is a challenging comparison for machines since humans accomplish the task outstandingly even on unknown words without any context. The goal of finding good machine-made models for stress assignment was set by applying new methods and by making use of a known theory about rules for stress assignment in Slovene. The upgraded data mining methods outperformed expert-defined rules on practically all subtasks, thus showing that data mining can more than compete with humans when constructing formal knowledge about stress assignment is concerned. Unfortunately, compared to humans directly, the data mining methods still failed to achieve as good results as humans on assigning stress to unknown words.

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.

This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was established by applying the hierarchical agglomerative clustering algorithm (HACA) (Han and Kamber, 2001) before the feature set changed. We aim to reach the result more efficiently than running HACA again from scratch on the feature-extended object set. Experiments testing the method's efficiency and a practical distributed systems problem in which the HAC method can be efficiently used (the problem of adaptive horizontal fragmentation in object oriented databases) are also reported.

In this paper, the relative multidimensional scaling method is investigated. This method is designated to visualize large multidimensional data. The method encompasses application of multidimensional scaling (MDS) to the so-called basic vector set and further mapping of the remaining vectors from the analyzed data set. In the original algorithm of relative MDS, the visualization process is divided into three steps: the set of basis vectors is constructed using the k-means clustering method; this set is projected onto the plane using the MDS algorithm; the set of remaining data is visualized using the relative mapping algorithm. We propose a modification, which differs from the original algorithm in the strategy of selecting the basis vectors. The experimental investigation has shown that the modification exceeds the original algorithm in the visualization quality and computational expenses. The conditions, where the relative MDS efficiency exceeds that of standard MDS, are estimated.

The Star Plot approach to high-dimensional data visualization is applied to multi-attribute dichotomies. It is observed that the areas of the plot for the two parts of a dichotomy may be used as an aggregate measure of their relative dominance. An optimization model is developed to determine a topology (or weighted configuration of the attributes) that maximizes the resolution of this measure with respect to a given set of reference dichotomies.