Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
There has been a growing interest in mining frequentitemsets in relational data with multiple attributes. A keystep in this approach is to select a set of attributes thatgroup data into transactions and a separate set of attributesthat labels data into items. Unsupervised and unrestrictedmining, however, is stymied by the combinatorial complexityand the quantity of patterns as the number of attributesgrows. In this paper, we focus on leveraging the semanticsof the underlying data for mining frequent itemsets. Forinstance, there are usually taxonomies in the data schemaand functional dependencies among the attributes. Domainknowledge and user preferences often have the potentialto significantly reduce the exponentially growing miningspace. These observations motivate the design of a user-directeddata mining framework that allows such domainknowledge to guide the mining process and control the miningstrategy. We show examples of tremendous reductionin computation by using domain knowledge in mining relationaldata with multiple attributes.