A　continuous　data　mining　based　on　a　session　model　generates　a　measure　sequence　of　first-order　rule.　The　parameter　estimation　for　the　measure　sequence　obtains　basic　characteristic　of　dynamic　evolution,　used　to　explain　the　interestingness　and　evolutional　regularity　of　the　rule.　The　information　diffusion　estimation　method　for　the　sequence　with　a　small　sample　is　proposed.　Being　one　of　higher　order　mining　technique,　it　attempts　to　solve　the　parameter　estimation　problem　of　measure　sequence　composed　of　incomplete　data　set,　based　on　the　principle　of　information　diffusion.　The　algorithms　are　considered　from　two　aspects　of　descriptive　modeling　and　predictive　modeling,　and　presented　for　the　diffusion　estimation　in　ascend/descend　trend,　using　the　measure　sequence　regarded　as　incomplete　sample.　Experiment　results　show　the　effectiveness,　fine　robustness　and　simplicity.　©　2006　IEEE.