  Associative memories are structures that store data patterns and retrieve
them given partial inputs. Sparse Clustered Networks (SCNs) are
recently-introduced binary-weighted associative memories that significantly
improve the storage and retrieval capabilities over the prior state-of-the art.
However, deleting or updating the data patterns result in a significant
increase in the data retrieval error probability. In this paper, we propose an
algorithm to address this problem by incorporating multiple-valued weights for
the interconnections used in the network. The proposed algorithm lowers the
error rate by an order of magnitude for our sample network with 60% deleted
contents. We then investigate the advantages of the proposed algorithm for
hardware implementations.
