Towards Dynamic Maintenance of Retrieval Knowledge in CBR

The utility problem is observable in many learning systems including case-based reasoning (CBR). In-dexing strategies have been implemented in CBR to overcome the effects of the utility problem but have been criticised as although improving retrieval effi-ciency they can reduce the competency of solutions and can be difficult to maintain. Here we present a novel indexing strategy based on a modified k-means clustering algorithm. We demonstrate that such an indexing strategy improves retrieval efficiency with-out adversely affecting solution competency. Impor-tantly it also provides a means for the dynamic real time maintenance of retrieval knowledge thus ensur-ing that the index is always optimal.