This　paper　studies　a　special　case　of　semi-supervised　text　categorization.　We　want　to　build　a　text　classifier　with　only　a　set　P　of　labeled　positive　documents　from　one　class　(called　positive　class)　and　a　set　U　of　a　large　number　of　unlabeled　documents　from　both　positive　class　and　other　diverse　classes　(called　negative　class).　This　kind　of　semi-supervised　text　classification　is　called　positive　and　unlabeled　learning　(PU-Learning).　Although　there　are　some　effective　methods　for　PU-Learning,　they　do　not　perform　very　well　when　the　labeled　positive　documents　are　very　few.　In　this　paper,　we　propose　a　refined　method　to　do　the　PU-Learning　with　the　known　technique　combining　Rocchio　and　K-means　algorithm.　Considering　the　set　P　may　be　very　small　(≤5%),　not　only　we　extract　more　reliable　negative　documents　from　U　but　also　enlarge　the　size　of　P　with　extracting　some　most　reliable　positive　documents　from　U.　Our　experimental　results　show　that　the　refined　method　can　perform　better　when　the　set　P　is　very　small.　©2010　IEEE.