As　an　extension　of　the　classical　set　theory,　rough　set　theory　plays　a　crucial　role　in　uncertainty　measurement.　In　this　paper,　concepts　of　information　entropy　and　mutual　information-based　uncertainty　measures　are　presented　in　both　complete　and　incomplete　information/decision　systems.　Then,　some　important　properties　of　these　measures　are　investigated,　relationships　among　them　are　established,　and　comparison　analyses　with　several　representative　uncertainty　measures　are　illustrated　as　well.　Theoretical　analysis　indicates　that　these　proposed　uncertainty　measures　can　be　used　to　evaluate　the　uncertainty　ability　of　different　knowledge　in　complete/incomplete　decision　systems,　and　then　these　results　can　be　helpful　for　understanding　the　essence　of　knowledge　content　and　uncertainty　measures　in　incomplete　information/decision　systems.　Thus,　these　results　have　a　wide　variety　of　applications　in　rule　evaluation　and　knowledge　discovery　in　rough　set　theory.