Andreu Sancho Homepage

The increasing bulk of data generation in industrial and scientific applications has fostered practitioners’ interest in mining large amounts of unlabelled data in the form of continuous, high speed, and time-changing streams of information. An appealing field is association stream mining, which models dynamically complex domains via rules without assuming any a priori structure. Different from the related frequent pattern mining field, its goal is to extract interesting associations among the forming features of such data, adapting these to the ever-changing dynamics of the environment in a pure online fashion--without the typical offline rule generation. These rules are adequate for extracting valuable insight which helps in decision making. It is a pleasure to detail Fuzzy-CSar, an online genetic fuzzy system (GFS) designed to extract interesting, quantitative rules from streams of samples. It evolves its internal model online, being able to quickly adapt its knowledge in the ...

The ever-increasing integration of technology in the different areas of science and industry has fostered practitioners the design of applications that generate stupendous amounts of data on-line (e.g., smart sensors or network monitoring just to mention two real cases). Extracting information from these data is key, in order to gain a better understanding of the processes that the data are describing. However, learning from these data poses new challenges to traditional data mining techniques, which are not designed to deal with data streams: data in which concepts and noise vary over time. In this regard, I am proud to present the supervised neural constructivist system ( SNCS ), a neural Michigan-style Learning Classifier System  that has been designed to provide a fast reaction capacity and adaptability to the distinct possible changes in concept (concept drifts) with varying noisy inputs.  Inheriting the intrinsically on-line fashion of Michigan-style learning clas...

We live in a technological world crowded of information. Every device we can think of can give us a bunch of such data, usually in the form of a flow or stream of information in, more or less, real time . In this particular situation classical knowledge discovery mechanisms (like our loved C4.5, a decision tree developed by Quinlan) are completely unable of extract a correct model of the situation. But, what is so special with flows of data? Following the words of Gama and Rodriques: a data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, following at high speed, and generated by non-stationary distributions in dynamic environments . So, to properly handle this kind of knowledge the learning algorithm has to learn on line and process massive amounts of data increasing the challenges to be faced. Let's hold one's breath w...