In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "importance" to the class, which not only facilitates well-performing classification procedure, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification.

This paper describes a simple agent-based model of opinion propagation within hierarchical social networks that exhibits a counter intuitive/unintended consequence of the influence of moderate leadership. In this phenomena, a population of highly polarized but balanced opinions governed by a moderate leader consistently ends in a state where the both the population and the leadership hold extreme opinions opposite to those initially held by the leadership. The experiments are done using our SNODA modeling framework (Social Networks and Opinion Dynamics Analysis tool), designed to perform agent-based modeling and analysis of opinion dynamics in complex social networks.

We illustrate with two simple examples how interactive evolutionary computation (IEC) can be applied to exploratory data analysis (EDA). IEC is particularly valuable in an EDA context because the objective function is by definite either unknown a priori or difficult to formalize. The first example IEC is used to evolve the "true" metric of attribute space. Indeed, the assumed distance function in attribute space strongly conditions the information content of a two-dimensional display of the data, regardless of the dimension reduction approach. The goal here is to evolve the attribute space distance function until "interesting" features of the data are revealed when a clustering algorithm is applied. In a second example, we show how a user can interactively evolve an auditory display of cluster data. In this example, we use IEC with genetic programming to evolve a mapping of data to sound functions in order to sonify qualities of data clusters.