ABSTRACT

More and more applications such as traffic modeling, military sensing and tracking, online data processing etc., generate a large amount of data streams every day. Efficient knowledge discovery of such datastreams is an emerging active research area in data mining with broad applications. Different from data in traditional static databases, data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. Due to the unique features of data stream, traditional data mining techniques which require multiple scans of the entire data sets can not be applied directly to mine stream data, which usually allows only one scan and demands fast response time.