  In this paper, we informally introduce dynamic mind-maps that represent a new
approach on the basis of a dynamic construction of connectionist structures
during the processing of a data stream. This allows the representation and
processing of recursively defined structures and avoids the problem of a more
traditional, fixed-size architecture with the processing of input structures of
unknown size. For a data stream analysis with association discovery, the
incremental analysis of data leads to results on demand. Here, we describe a
framework that uses symbolic cells to calculate associations based on
transactional data streams as it exists in e.g. bibliographic databases. We
follow a natural paradigm of applying simple operations on cells yielding on a
mind-map structure that adapts over time.
