Activity is central to thought and cognition. Through interaction autonomous agents build working representations of the environment they inhabit (Trajkovski 2007). TamTam is a software demo based on interactionist principles (Bickhard 1980) based on unsupervised learning (Buisson 2006). This applet was developed to recognize and anticipate rhythmic patterns entered via a computer keyboard. TamTam always starts with a basic set of rhythms (e.g., four full-notes), and then uses a sophisticated algorithm for generating more and more complicated child patterns, based on the previously recognized patterns input by the user. Patterns that are successfully recognized are the “winners” and are used as the parents for the next generation of more sophisticated patterns, in an approach reminiscent of genetic algorithms. We viewed TamTam not only as an demonstrative simulation, but also as a possible learning paradigm in pattern recognition and anticipation. We studied the efficacy of converting this approach into a generalized pattern recognition tool to be used for pattern mining for large datasets, including sets of bioinformatics data such as gene sequences.