Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams

摘要

This paper illustrated deep learning Semi-structured Tree Miner for Data Stream's algorithm focused on frequent pattern mining used in data streams on semi-structured data. In the target time, the algorithm obtains a frequent pattern. The Semistructured Tree Miner for Data Streams algorithm can handle sliding windows use the Time attenuation model to minimise historical data on the mining process relative to traditional algorithms, following the concept of inclusion and exclusion conservation at the same time and full real-time mining operations. And it is effective.