Re-mining of ontological association rules under support threshold refinement

[[abstract]]The problem of mining association rules incorporated with domain knowledge (ontology) has attracted lots of researchers’attention recently. In our previous work, we have investigated this problem and devised two efficient algorithms, called AROC and AROS, for mining association rules with ontological information that presents not only classification but also composition relationship. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover informative rules. As such, how to shorten the response time for each remining process is a crucial issue for realizing interactive mining environment. In this paper, we continue the study of ontological association mining towards is avenue. An incremental algorithm is proposed. Empirical evaluations showed that the proposed algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and is more efficient than running AROC or AROS afresh