An Efficient Algorithm for Mining Frequent Itemests over the Entire History of Data Streams
Hua-Fu Li, Suh-Yin Lee, Man-Kwan Shan · 2004
A data stream is a continuous, huge, fast changing, rapid, infinite sequence of data elements. The nature of streaming data makes it essential to use online algorithms which require only one scan over the data for knowledge discovery. In this paper, we propose a new single-pass algorithm, called DSMFI (Data Stream Mining for Frequent Itemsets), to mine all frequent itemsets over the entire history of data streams. DSM-FI has three major features, namely single streaming data scan for counting itemsets’ frequency information, extended prefix-tree-based compact pattern representation, and top-down frequent itemset discovery scheme. Our performance study shows that DSM-FI outperforms the well-known algorithm Lossy Counting in the same streaming environment.