关键词: 数据挖掘，数据流，频繁模式，震荡性因子

Abstract: Mining frequent patterns from data streams is one of the hottest research topics in data mining nowadays.The features of data streams, such as consecution, disorder and real-time, raise requirements for higher time and space performance of mining algorithms. Vibration of pattern frectuency in data streams, compels the present algorithms to revise the synopsis structure continually,and leads up to disadvantage impact on both time and space efficiency. A more scalable synopsis structure SP-tree was designed firstly, meanwhile the concept of vibration factor X was given for main-twining vibrational information. Then an efficient algorithm for mining frequent patterns over offline data streams SPDS was proposed, which relieves the performance from the impact of vibration effectively, and increases the count accuracy of partial patterns. This algorithm adopts a dividcand-conquer mechanism to mine the current datasct, thereby improves itself further.

Key words: Data ming,Data stream,Frecauent pattern(FP)，Vibration factor