Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

Characterization of the interfaces between Au(hkl) single crystal basal plane electrodes and [Emmim][Tf2N] ionic liquid

The interface between Au(hkl) basal planes and the ionic liquid 1-Ethyl-2,3-dimethyl imidazolium bis(trifluoromethyl)sulfonil imide was investigated by using both cyclic voltammetry and laser-induced temperature jump. Cyclic voltammetry showed characteristic features, revealing surface sensitive processes at the interfaces Au(hkl)/[Emmim][Tf2N]. From laser-induced heating the potential of maximum entropy (pme) is determined. Pme is close to the potential of zero charge (pzc) and, therefore, the technique provides relevant interfacial information. The following order for the pme values has been found: Au(111) > Au(100) > Au(110). This order correlates well with work function data and values of pzc in aqueous solutions.

Interfaces for Intelligent Environments

Presentaciones de la asignatura Interfaces para Entornos Inteligentes del Máster en Tecnologías de la Informática/Machine Learning and Data Mining.