Abstract: A recent work has introduced the problem of mining neighboring class sets,where instances of each class of a neighboring class set are grouped using their Euclidean distances from each other.Although the concept of neighboring class sets is a useful one,the effective computation of frequent neighboring class sets is only partially solved.A novel algorithm for mining frequent neighboring class sets from spatial datasets is presented.Compared to the previous algorithm,the algorithm can discover complete and correct frequent neighboring class sets.