Gradual itemsets of the form “the more/less A, the more/less B” extract knowledge in the form of correlations between attributes. The methods for extracting such item sets can generate contradictory itemsets, for example simultaneously producing the itemsets “the more A, the more B” and “the more A, the less B”. To process these contradictions, we propose a constrained definition of the gradual itemset support. In particular, it does not only depend on the considered itemset, but also on its potential contradictors. An algorithm to efficiently compute the proposed global proper gradual support is defined, as well as two methods for extracting frequent gradual itemsets according to this new support definition. Experimental results obtained from a real dataset highlight the relevance of the approach.