Recently,　categorization　methods　based　on　association　rules　have　been　given　much　attention.　In　general,　association　classification　has　the　higher　accuracy　and　the　better　performance.　However,　the　classification　accuracy　drops　rapidly　when　the　distribution　of　feature　words　in　training　set　is　uneven.　Therefore,　text　categorization　algorithm　Weighted　Association　Rules　Categorization　(WARC)　is　proposed　in　this　paper.　In　this　method,　association　rules　are　used　to　classify　training　samples　and　rule　intensity　is　defined　according　to　the　number　of　misclassified　training　samples.　Each　strong　rule　is　multiplied　by　factor　less　than　1　to　reduce　its　weight　while　each　weak　rule　is　multiplied　by　factor　more　than　1　to　increase　its　weight.　The　result　of　research　shows　that　this　method　can　remarkably　improve　the　accuracy　of　association　classification　algorithms　by　regulation　of　rules　weights.　©　Springer-Verlag　Berlin　Heidelberg　2005.