An Efficient Xml Database Mining without Candidate Generation: an Frequent Pattern Split Approach

The popularity of XML results in producing large numbers of XML documents. Therefore, to develop an approach of association rule mining on native XML databases is an important research. The FP-growth based on an FP-tree algorithm performs more efficiently than other methods of association rules mining, but it cannot be applied to native XML databases. Hence, we adaptive an improving FPtree algorithm called Frequent Pattern Split method, simply FP-split, for fast association rule mining from native XML databases. We show that the FP-split method is time-efficient for mining association rules from native XML databases by experiments with various parameters, such as various minimum supports, different number of items, and large amount of data. In addition, we also implement a lot of experiments to show that our proposed method performs better than FP-tree construction algorithm in transaction database.