Updating generalized association rules with evolving taxonomies
Applied Intelligence
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Data mining is an interactive and iterative process. It is highly probable that a user will issue a series of similar queries until he or she receives satisfying results. Currently available mining algorithms suffer from long processing times depending mainly on the size of the dataset. Asthe pattern discovery takes place mainly in the data warehouse environment, such long processing times are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are usually very similar. This observation leads to the idea of reusing materialized results of previous data mining queries in order to improve performance of the system. In this paper we present the concept of materialized data mining views and we show how the results stored in these views can be used to accelerate processing of data mining queries. We demonstrate the use of materializedviews in the domains of association rules discovery and sequential pattern search.