Kernel　least　mean　square　is　a　simple　and　effective　adaptive　algorithm,　but　dragged　by　its　unlimited　growing　network　size.　Many　schemes　have　been　proposed　to　reduce　the　network　size,　but　few　takes　the　distribution　of　the　input　data　into　account.　Input　data　distribution　is　generally　important　in　view　of　both　model　sparsification　and　generalization　performance　promotion.　In　this　paper,　we　introduce　an　online　density-dependent　vector　quantization　scheme,　which　adopts　a　shrinkage　threshold　to　adapt　its　output　to　the　input　data　distribution.　This　scheme　is　then　incorporated　into　the　quantized　kernel　least　mean　square　(QKLMS)　to　develop　a　density-dependent　QKLMS　(DQKLMS).　Experiments　on　static　function　estimation　and　short-term　chaotic　time　series　prediction　are　presented　to　demonstrate　the　desirable　performance　of　DQKLMS.