一种面向数据流的频繁项集挖掘算法

An Algorithm for Mining Frequent Itemsets in Data Streams

与传统静态数据库中的数据不同,数据流是一个按时间到达的有序的项集,这使得经典的频繁项集挖掘算法难以适用到数据流中.根据数据流的特点,提出了数据流频繁项集挖掘算法FP-SegCount.该算法将数据流分段并利用改进的FP-growth算法挖掘分段中的频繁项集.然后,利用Count Min Sketch进行项集计数.算法解决了压缩统计和计算快速高效的问题.通过和FP-DS算法的实验对比,FP-SegCount算法具有较好的时间效率.

Different from data in traditional static database, a data stream is an ordered sequence of items that arrives in timely order. Classical frequent item - sets mining method is difficult to apply to data stream. Based on the characteristics of data streams, FP - SegCount algorithm is proposed in this paper to mine frequent item - sets from data streams. The algorithm partitions the data stream and uses modified FP - growth algorithm to mine frequent item- sets in every segment. It then counts item -sets in Count Min Sketch. This algorithm solves compressed statistics and ensures effective computation. Through experimentation and comparison with FP - DS algorithm, FP SegCount algorithm is shown to have a good time efficiency.