The invention discloses an on-line attribute abnormal point detecting method for supporting dynamic update. The method comprises the following steps of: providing brand-new attribute abnormal point definition by analyzing practical application and user requirement, detecting an abnormal point on the premise of taking an attribute correlation between data points in a data set into consideration, providing more effective abnormal information relative to the traditional definition, and supporting on-line attribute abnormal point detection for dynamically updated stream data by combining practical stream data system application and using a sliding window and an on-line clustering method so as to provide real-time detection result feedback for a user. Aiming at the practical system overload condition in the stream data system application, a method for effectively reducing the load is provided so that the detecting method still can feed back the detection result in real time under the mass stream data update condition, the result error is in a user controlled range and effective balance of the detecting method between the running efficiency and the result precision is achieved.