Discovering　semantic　knowledge　is　significant　for　understanding　and　interpreting　how　people　interact　in　a　meeting　discussion.　In　this　paper,　we　propose　a　mining　method　to　extract　frequent　patterns　of　human　interaction　based　on　the　captured　content　of　face-to-face　meetings.　Human　interactions,　such　as　proposing　an　idea,　giving　comments,　and　expressing　a　positive　opinion,　indicate　user　intention　toward　a　topic　or　role　in　a　discussion.　Human　interaction　flow　in　a　discussion　session　is　represented　as　a　tree.　Tree-based　interaction　mining　algorithms　are　designed　to　analyze　the　structures　of　the　trees　and　to　extract　interaction　flow　patterns.　The　experimental　results　show　that　we　can　successfully　extract　several　interesting　patterns　that　are　useful　for　the　interpretation　of　human　behavior　in　meeting　discussions,　such　as　determining　frequent　interactions,　typical　interaction　flows,　and　relationships　between　different　types　of　interactions.