Adaptive fuzzy partitions for evolving association rules in big data stream

The amount of data being generated in industrial and scientific applications is constantly increasing. These are often generated as a chronologically ordered unlabeled data flow which exceeds usual storage and processing capacities. Association stream mining is an appealing field which models complex environments online by finding relationships among the attributes without presupposing any a priori structure. The discovered relationships are continuously adapted to the dynamics of the problem in a pure online way, being able to deal with both categorical and continuous attributes. This paper presents a new advanced version, Fuzzy-CSar-AFP, of an online genetic fuzzy system designed to obtain interesting fuzzy association rules from data streams. It is capable of managing partitions of different granularity for the variables, which allows the algorithm to adapt to the precision requirements of each variable in the rule. It can also work with data streams without needing to know the domains of the attributes as it includes a mechanism which updates them in real-time. Fuzzy-CSar-AFP performance is validated in an original real-world Psychophysiology problem where associations between different electroencephalogram signals in subjects which are put through different stimuli are analyzed.