Mining for Contiguous Frequent Itemsets in Transaction Databases

Mining a transaction database for association rules is a particularly popular data mining task, which involves the search for frequent co-occurrences among items. One of the problems often encountered is the large number of weak rules extracted. Item taxonomies, when available, can be used to reduce them to a more usable volume. In this paper we introduce a new data mining paradigm, which involves the discovery of contiguous frequent itemsets. We formulate the problem of mining contiguous frequent itemsets in a transaction database and we present a level-wise algorithm for finding these itemsets. Contiguous frequent itemsets may contain important knowledge about the dataset, that can not be exposed by the use of classic association rule mining approaches. This knowledge may well include serious hints for the generation of a taxonomy for all or part of the items.