This　paper　presents　an　adaptive　grid-based　clustering　algorithm　called　as　＂AGFC＂,　which　uses　a　forest-like　query　structure　to　sequentially　discovers　multiple　arbitrary-shaped　clusters　from　the　grid.　The　main　advantage　of　AGFC　is　that　it　can　effectively　generate　a　reasonable　grid　division　with　a　simple　startup　parameter.　This　method　determines　the　appropriate　grid　division　width　through　the　minimum　gap　between　the　peaks　and　valleys　of　the　density　curve　in　a　specific　dimension,　which　depends　on　the　distribution　of　the　sample,　to　overcome　the　subjectivity　of　manual　determination　to　a　certain　extent.　Furthermore,　in　the　forest-like　query　structure,　it　constructs　a　＂Aggregation　Judgment＂　criterion　for　high-density　cells　to　find　out　the　possible　clusters　through　the　merging　of　cells.　Finally,　using　the　＂Re　clustering　process＂　to　eliminate　very　small　clusters　and　further　repairing　the　edge　areas　of　the　main　clusters.　The　experimental　results　show　that　the　proposed　method　can　obtain　competitive　results　under　the　premise　of　automatically　determining　the　grid.　(C)　2022　Elsevier　B.V.　All　rights　reserved.