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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 ...

What do the customers buy? Which products are bought together? With these two short questions the field of association rule (AR) mining makes its appearance. In this field of ML, the original aim was to find associations and correlations between the different items that customers place in their shopping market. More generally, the goal of AR is to find frequent and interesting patterns , associations , correlations , or causal structures among sets of items or elements in large databases and put these relationships in terms of association rules . AR is an important part of the unsupervised learning paradigm, so the algorithm has not the presence of an expert to teach it during the training stage. Why AR mining may be so important ? Many commercial applications generate huge amounts of unlabeled data (just think of Facebook for a moment), so our favorite classifier system will not work in this environment. With AR we can exploit such databases and extract any kind of useful in...