Case-base maintenance in an associative memory organized by a self-organization map

Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved in a case memory, where each case represents a solved situation. Therefore, the case memory size and its organization influences on the computational time needed to solve new situations. For this reason, we organize the memory using a Self-Organization Map for defining patterns to allow system to do a selective retrieval using only the cases of the most suitable pattern. This works presents a case-based maintenance to incrementally introduce knowledge in SOM without retraining it because this process is very expensive in terms of computational time. The strategy is semi-supervised because we use the feedback provided by the expert and, at the same time, the self-organization of cases when clusters are readjusted. Results show a successful case-based maintenance.