Frequent Pattern Mining in Data Streams

As the volume of digital commerce and communication has exploded, the demand for data mining of streaming data has likewise grown. One of the fundamental data mining tasks, for both static and streaming data, is frequent pattern mining. The goal of pattern mining is to identity frequently occurring patterns and structures. Such patterns may indicate scientific phenomena, economic or social trends, or even security threats. Moreover, not only is pattern discovery important by itself, but it is also a building block for machine learning tasks such as association rule induction. Traditionally, algorithms for pattern discovery have processed the entire dataset as a batch, with no restriction on how many passes through the data would be taken.

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