  We propose a new measure of support (the number of occur- rences of a
pattern), in which instances are more important if they occur with a certain
frequency and close after each other in the stream of trans- actions. We will
explain this new consecutive support and discuss how patterns can be found
faster by pruning the search space, for instance using so-called parent support
recalculation. Both consecutiveness and the notion of hypercliques are
incorporated into the Eclat algorithm. Synthetic examples show how interesting
phenomena can now be discov- ered in the datasets. The new measure can be
applied in many areas, ranging from bio-informatics to trade, supermarkets, and
even law en- forcement. E.g., in bio-informatics it is important to find
patterns con- tained in many individuals, where patterns close together in one
chro- mosome are more significant.
