Hypothesis Testing Based Knowledge Discovery in Distributed Multiple Data Sources

In the past several years, most data mining researchers focus on data mining from single data source. Nowadays, data mining from multiple data sources is a new problem in Web environment and is also an efficient technique for solving knowledge discovery in distributed databases. A new method for mining multi-data sources is presented in this paper. By sharing knowledge patterns discovered in other similar data sources, hypothesis testing is employed for verifying whether the patterns are also suitable for local data source or not. So that can improve the efficiency of KDD greatly. Finally the effectiveness of this method is analyzed and experimental result is given. This method can be extended as an efficient data mining algorithm in case of apriori hypothesizes are provided. And it can be also used for incremental data mining.