26:T4b4,

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of mining concept-drifting data streams. However, most of these approaches can only be applied to uni-dimensional classification problems where each input instance has to be assigned to a single output class variable. The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced. In this paper, we propose a novel adaptive method, named Locally Adaptive-MB-MBC (LA-MB-MBC), for mining streaming multi-dimensional data. To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models. Basically, LA-MB-MBC monitors the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a concept drift is detected, LA-MB-MBC adapts the current MBC network locally around each changed node. An experimental study carried out using synthetic multi-dimensional data streams shows the merits of the proposed method in terms of concept drift detection as well as classification performance.

27:T449,

Attribute reduction is a very important task in Rough Set Theory. In this context, in recent years several attribute reduction algorithms based on the discernibility matrix have been proposed. In this paper, we go back to the binary discernibility matrix and reformulate some basic concepts that allow us to build on them an algorithm for computing reducts. The proposed algorithm takes advantage of the binary representation used for the discernibility matrix and depending on the fulfillment of certain pruning properties several candidates can be jumped (pruned). In this way, the number of candidates to be tested is reduced. Additionally, from the binary discernibility matrix, we define a more compact and ordered matrix, which allows reducing even more the amount of candidates to be evaluated, and the cost of verifying each one of them.

The proposed algorithm is able to compute all the reducts of an information table, but it can also be used to find a reduct, or a certain number of them. Experiments over standard datasets show that our algorithm has a good performance.

28:T53c,

In the coming years, the share of hybrid electric vehicles is expected to grow significantly in personal transportation. Vehicles that can be charged from the electrical grid, such as plug-in hybrids, could introduce problems for the distribution network, especially if the vehicle adoption is spatially concentrated and the charging happens unmanaged. Thus, where and when the hybrid vehicle adoption occurs is an important question for policy makers and planners.

Currently, promoted by the European Union directives such as INSPIRE and PSI, there is a trend of public sector data being harmonised and opened for free usage across the Europe. The vast amount of information in the various registers of the society has a huge and largely untapped potential for modelling societal and environmental phenomena. To discover correlations and relationships from such large databases, data mining methods can be useful.

In this paper, we utilise a data mining approach to identify relationships from public sector data between hybrid vehicle adoption and small area level socio-demography. Based on the discovered model, we assess how favourable each area type is for the adoption. The approach combines Self-Organizing Map based data compression, a feature selection using Genetic Algorithm and a linear regression model.

29:T669,