Data mining is an interactive and iterative process. A user defines a 
set of interesting patterns choosing the dataset to be mined and 
setting the values of various parameters that drive mining algorithm. 
It is highly probable that a user will issue the same mining query 
several times until he receives satisfying results. During each run 
a user will slightly modify either the definition of the mined dataset 
or the parameters of the algorithm. Currently available mining algorithms 
suffer from long processing times depending mainly on the size of the 
dataset. As the 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 very similar. 
One possible solution is to reuse materialized results of previous 
data mining queries. 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 materialized views in the domains of association rules discovery 
and sequential pattern search.