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    题名: An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams
    作者: 沈錳坤
    贡献者: 國立政治大學資訊科學系
    关键词: Mining;Frequent Itemsets;History of Data Streams
    日期: 2004-09
    上传时间: 2010-05-27 16:50:45 (UTC+8)
    摘要: 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 DSM- FI (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.
    關聯: First International Workshop on Knowledge Discovery in Data Streams, in conjunction with the European Conference on Machine Learning (ECML) and the European Conference on the Principals and Practice of Knowledge Discovery in Dataabse (PKDD)
    数据类型: conference
    显示于类别:[資訊科學系] 會議論文

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