Algorithm of frequent-patterns mining in data stream
一种数据流中的频繁模式挖掘算法

The limitlessness, mobility, and irregularity of time series data stream make the traditional frequent-pattern mining algorithms difficult to extend to the mining problem of time series data stream. According to the characteristics of time series data stream, a new algorithm for mining the frequent-pattern from a kind of special irregular data stream was proposed, in which, time series data stream was partitioned firstly, and then the local frequent items were mined step by step. Finally, the global frequent items could be mined efficiently based on these local frequent items. After applying the new algorithm in the revenue assurance project of telecommunication field, the results show that the new algorithm has good performance, and can mine frequent-patterns effectively from the irregular data stream of telecommunication field.