Mining Association Rules in Long Sequences

Abstract. Discovering interesting patterns in long sequences, and finding confident association rules within them, is a popular area in data mining. Most existing methods define patterns as interesting if they occur frequently enough in a sufficiently cohesive form. Based on these frequent patterns, association rules are mined in the traditional manner. Recently, a new interestingness measure, combining cohesion and frequency of a pattern, has been proposed, and patterns are deemed interesting if encountering one event from the pattern implies with a high probability that the rest of the pattern can be found nearby. It is quite clear that this probability is not necessarily equally high for all the events making up such a pattern, which is why we propose to introduce the concept of association rules into this problem setting. The confidence of such an association rule tells us how far on average from a particular event, or a set of events, one has to look, in order to find the res...

• Type: Conference
• Year: 2010
• Where: PAKDD
• Authors: Boris Cule, Bart Goethals
• Keywords: Association Rules, Confident Association Rules, Data Mining, Interesting, PAKDD 2010