Displaying Co-occurrences of Patterns in Streams for Website Usage Analysis

One way of getting a better view of data is by using frequent patterns. In this paper frequent patterns are (sub)sets 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 harder to say if a pattern is frequent 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 competitive neural network methods to online model pattern co-occurrence in a stream of itemsets. A model of the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so often together that they should form a combined pattern. In this way the patterns in the clustering will approximate the largest frequent patterns: maximal frequent patterns. The number of (approximated) maximal frequent patterns is much smaller and combined with methods of visualization using competitive neural networks these patterns provide a good view on the structure of the stream.