Fuzzy Frequent Pattern Discovering Based on Recursive Elimination
Xiaomeng Wang, Christian Borgelt, Rudolf Kruse · 2006
Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent patterns cannot be discovered, since with exact matching the number of supporting transactions may be smaller than the user-specified minimum. In order to allow approximate matching during the mining process, we propose an approach based on transaction editing. Our recursive algorithm relies on a step by step elimination of items from the transaction database together with a recursive processing of transaction subsets. This algorithm works without complicated data structures and allows us to find fuzzy frequent patterns easily.