Mining Frequent Patterns in Data Streams

Finding frequent items is one of the most basic problems in the data streams. The limitless and mobility of data streams make the traditional frequent-pattern algorithm difficult to extend to data streams. According to data streams characteristic, inspired by the fact that the FP-growth provides an effective algorithm for frequent pattern mining, a new FP-DS algorithm for mining frequent patterns from data streams is proposed. In addition, the method, in which data streams are partitioned and frequent items are mined step by step, is adopted in the algorithm. So users may continuously get present frequent items online and any length frequent patterns for data streams can effectively be mined. Through introducing error ε, a large number of non- frequent items will be cut down and the storage space of the data streams can be reduced. Based on this algorithm, the error of support is guaranteed not to exceed ε. The analysis and experiments show that this algorithm has good performance.