Abstract
The scope of this work is the segmentation of the orders of Bivolino, a Belgian company that sells custom tailored shirts. The segmentation is done based on clustering, following a Data Mining approach. We use the K-Medoids clustering method because it is less sensitive to outliers than other methods and it can handle nominal variables, which are the most common in the data used in this work. We interpret the results from both the design and marketing perspectives. The results of this analysis contain useful knowledge for the company regarding its business. This knowledge, as well as the continued usage of clustering to support both the design and marketing processes, is expected to allow Bivolino to make important business decisions and, thus, obtain competitive advantage over its competitors. © Springer International Publishing Switzerland 2013.

Abstract
The success of the textile industry largely depends on the products offered and on the speed of response to variations in demand that are induced by changes in consumer lifestyles. The study of behavioral habits and buying trends can provide models to be integrated into the decision support systems of companies. Data mining techniques can be used to develop models based on data. This approach has been used in the past to develop models to improve sales in the textile industry. However, the discovery of scientific models based on subgroup discovery algorithms, that characterize subgroups of observations with rare distributions, has not been made in this area. The goal of this work is to investigate whether these algorithms can extract knowledge that is useful for a particular kind of textile industry, which produces highly customized garments. We apply the CN2-SD subgroup discovery method to find rare and interesting subgroups products on a database provided by a manufacturer of custom-made shirts. The results show that it is possible to obtain knowledge that is useful to understand customer preferences in highly customized textile industries using subgroup discovery techniques. © Springer International Publishing Switzerland 2013.

Abstract
Several approaches have been applied to the task of algorithm selection. In this context, Meta-Learning provides an efficient solution by adopting a supervised strategy. Despite its promising results, Meta-Learning requires an adequate number of instances to produce a rich set of meta-examples. Recent approaches to generate synthetic or manipulated datasets have been adopted with success in the context of Meta-Learning. These proposals include the datasetoids approach, a simple data manipulation technique that generates new datasets from existing ones. Although such proposals can actually produce relevant datasets, they can eventually produce redundant, or even irrelevant, problem instances. Active Meta-Learning arises in this context to select only the most informative instances for meta-example generation. In this work, we investigate the Active Meta-Learning combined with datasetoids, focusing on using the Random forest algorithm in meta-learning. Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples and obtain a significant gain in Meta-Learning performance.

Abstract
One of the hardest resources to manage in retail is space. Retailers need to assign limited store space to a growing number of product categories such that sales and other performance metrics are maximized. Although this seems to be an ideal task for a data mining approach, there is one important barrier: the representativeness of the available data. In fact, changes to the layout of retail stores are infrequent. This means that very few values of the space variable are represented in the data, which makes it hard to generalize. In this paper, we describe a Decision Support System to assist retailers in this task. The system uses an Evolutionary Algorithm to optimize space allocation based on the estimated impact on sales caused by changes in the space assigned to product categories. We assess the quality of the system on a real case study, using different regression algorithms to generate the estimates. The system obtained very good results when compared with the recommendations made by the business experts. We also investigated the effect of the representativeness of the sample on the accuracy of the regression models. We selected a few product categories based on a heuristic assessment of their representativeness. The results indicate that the best regression models were obtained on products for which the sample was not the best. The reason for this unexpected results remains to be explained. © 2013 Springer-Verlag.

Abstract
Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.

Abstract