Welcome to the home page of the Spatial Contextualization for Closed Itemset Mining (SCIM) algorithm and related techniques.
The SCIM algorithm is a mining procedure that builds a space for the target database in such a way that relevant closed itemsets can be retrieved regarding the relative spatial location of their items. More specifically, our approach uses Dual Scaling to map the items of the database to a multidimensional metric space called Solution Space. The representation of the database in the Solution Space assists in the interpretation and definition of overlapping clusters of related items. The distances of the items to the centers of the clusters are used as criteria for generating itemsets. Therefore, instead of using the minimum support threshold, a distance threshold is defined concerning the reference and the maximum distances computed per cluster during the mapping procedure.
The SCIM algorithm was developed by Altobelli B. Mantuan and Leandro A. F. Fernandes. In a previous work, Leandro and Ana Cristina Bicharra Garcia used the same Solution Space to present association rules with some semantic contextualization that assists interpretation of the mined rules.
Here you find all publicly available material about the SCIM and related techniques.