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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 ...

From the classic point of view, machine learning algorithms are classified based on the desired outcome of the algorithm. There are three main types of learning: supervised learning, where an expert or teacher provides feedback in the learning process, unsupervised learning, where there is no expert or teacher when the learning process is running, and reinforcement learning, where the program learns interacting with the environment. The latter technique of learning is a fundamental mechanism in learning classifier systems (LCSs). These are cognitive systems that receive perceptions from the environment and, in response to these, perform actions to solve the problem that are facing. Originally proposed by John Holland and later simplified by David Goldberg and others, LCSs are computer programs that are based on observations of how natural selection processes and Darwinian evolution solve complex tasks. The original purpose was to create true artificial intelligence mimicking the adapt...