Traditional multiple query optimization methods focus on identifying common subexpressions in sets of relational queries and on constructing their global execution plans. In this paper we consider the problem of optimizing sets of data mining queries submitted to a Knowledge Discovery Management System. We describe the problem of data mining query scheduling and we introduce a new algorithm called CCAgglomerative to schedule data mining queries for frequent itemset discovery.