  One way of getting a better view of data is using frequent patterns. In this
paper frequent patterns are subsets that occur a minimal number of times in a
stream of itemsets. However, the discovery of frequent patterns in streams has
always been problematic. Because streams are potentially endless it is in
principle impossible to say if a pattern is often occurring or not. Furthermore
the number of patterns can be huge and a good overview of the structure of the
stream is lost quickly. The proposed approach will use clustering to facilitate
the analysis of the structure of the stream.
  A clustering on the co-occurrence of patterns will give the user an improved
view on the structure of the stream. Some patterns might occur so much together
that they should form a combined pattern. In this way the patterns in the
clustering will be the largest frequent patterns: maximal frequent patterns.
  Our approach to decide if patterns occur often together will be based on a
method of clustering when only the distance between pairs is known. The number
of maximal frequent patterns is much smaller and combined with clustering
methods these patterns provide a good view on the structure of the stream.
