Abstract
The study of evolution has become an important research issue; especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a elms of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory; and a transition detection algorithm. To demonstrate its feasibility and applicability we present; real world case studies, using datasets extracted from Banco de Portugal and the Portuguese Institute of Statistics. We also test our approach in a benchmark dataset from TSDL. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions.

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Sensor data is usually represented by streaming time series. Current state-of-the-art systems for visualization include line plots and three-dimensional representations, which most of the time require screen resolutions that are not available in small transient mobile devices. Moreover, when data presents cyclic behaviors, such as in the electricity domain, predictive models may tend to give higher errors in certain recurrent points of time, but the human-eye is not trained to notice this cycles in a long stream. In these contexts, information is usually hard to extract from visualization. New visualization techniques may help to detect recurrent faulty predictions. En this paper we inspect visualization techniques in the scope of a real-world sensor network, quickly dwelling into future trends in visualization in transient mobile devices. We propose a simple dense pixel display visualization system, exploiting the benefits that it may represent on detecting and correcting recurrent faulty predictions. A case study is also presented, where a simple corrective strategy is studied in the context of global electrical load demand, exemplifying the utility of the new visualization method when compared with automatic detection of recurrent errors.

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Background: Within Rosaceae, the RNase based gametophytic self-incompatibility (GSI) system has been studied at the molecular level in Maloideae and Prunus species that have been diverging for, at least, 32 million years. In order to understand RNase based GSI evolution within this family, comparative studies must be performed, using similar methodologies. Result: It is here shown that many features are shared between the two species groups such as levels of recombination at the S-RNase ( the S-pistil component) gene, and the rate at which new specificities arise. Nevertheless, important differences are found regarding the number of ancestral lineages and the degree of specificity sharing between closely related species. In Maloideae, about 17% of the amino acid positions at the S-RNase protein are found to be positively selected, and they occupy about 30% of the exposed protein surface. Positively selected amino acid sites are shown to be located on either side of the active site cleft, an observation that is compatible with current models of specificity determination. At positively selected amino acid sites, non-conservative changes are almost as frequent as conservative changes. There is no evidence that at these sites the most drastic amino acid changes may be more strongly selected. Conclusions: Many similarities are found between the GSI system of Prunus and Maloideae that are compatible with the single origin hypothesis for RNase based GSI. The presence of common features such as the location of positively selected amino acid sites and lysine residues that may be important for ubiquitylation, raise a number of issues that, in principle, can be experimentally addressed in Maloideae. Nevertheless, there are also many important differences between the two Rosaceae GSI systems. How such features changed during evolution remains a puzzling issue.