Incremental Mining of Ontological Association Rules in Evolving Environments

The process of knowledge discovery from databases is a knowledge intensive, highly user-oriented practice, thus has recently heralded the development of ontology-incorporated data mining techniques. In our previous work, we have considered the problem of mining association rules with ontological information (called ontological association rules) and devised two efficient algorithms, called AROC and AROS, for discovering ontological associations that exploit not only classification but also composition relationship between items. The real world, however, is not static. Data mining practitioners usually are confronted with a dynamic environment. New transactions are continually added into the database over time, and the ontology of items is evolved accordingly. Furthermore, the work of discovering interesting association rules is an iterative process; the analysts need to repeatedly adjust the constraint of minimum support and/or minimum confidence to discover real informative rules. Under these circumstances, how to dynamically discover association rules efficiently is a crucial issue. In this regard, we proposed a unified algorithm, called MIFO, which can handle the maintenance of discovered frequent patterns taking account of all evolving factors: new transactions updating in databases, ontology evolution and minimum support refinement. Empirical evaluation showed that MIFO is significantly faster than running our previous algorithms AROC and AROS from scratch.