Maintenance algorithm for updating the discovered multiple fuzzy frequent itemsets for transaction deletion
Jerry Chun‐Wei Lin, Tsu‐Yang Wu, Guo Lin, Tzung‐Pei Hong · 2014
Fuzzy set theory was adopted to induce natural and understandable linguistic rules from the transactions with quantitative values. In the past, many algorithms were proposed to mine the desired fuzzy association rules from a static database. In real-world applications, transactions may, however, be inserted into or deleted from an original database. The discovered information is required to be re-mined in batch mode. In this paper, a maintenance algorithm for efficiently updating the discovered multiple fuzzy frequent itemsets is thus proposed. Based on the FUP2 concepts for transaction deletion, the proposed maintenance algorithm has better performance compared to the Apriori-based algorithm.