The large amount of patterns generated by frequent pattern mining algorithms has been extensively addressed in the last few years. In geographic pattern mining, besides the large amount of patterns, many are well known geographic domain associations. Existing algorithms do not warrant the elimination of all well known geographic dependences since no prior knowledge is used for this purpose. This paper presents a two step method for mining frequent geographic patterns without associations that are previously known as non-interesting. In the first step the input space is reduced as much as possible. This is as far as we know still the most efficient method to reduce frequent patterns. In the second step, all remaining geographic dependences that can only be eliminated during the frequent set generation are removed in an efficient way. Experiments show an elimination of more than 50% of the total number of frequent patterns, and which are exactly the less interesting.

In frequent geographic pattern mining a large amount of patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without associations that are previously known as non- interesting. Geographic dependences are eliminated during the frequent set generation using prior knowledge. After the dependence elimination maximal generalized frequent sets are computed to remove redundant frequent sets. Experimental results show a significant reduction of both the number of frequent sets and the computational time for mining maximal frequent geographic patterns.