Mining Burst patterns in large temporal database
大型时态数据库中的Burst 模式挖掘

How to effectively discover potentially useful knowledge from large databases is an important yet challenging issue. The paper firstly pointed out there would be two problems in mining very large temporal databases with experimental results, and then proposed a new method to solve the two problems. This approach first partitioned a database into several small datasets. And then the Burst patterns were dug up after four times pruning on the data. The experimental results show that the proposed method is accurate and efficient, and the Burst patterns are useful for decision-making in business.