An Efficient Association Rule Mining For XML Data

Abstract

XML association rule mining is an important problem in data mining domain. Currently, the problem of association rule mining on XML data has not been well studied. In this paper, we proposed an efficient association rule mining for XML data which mining association rule in large amount of XML data. The set of data is view as a binary table. The value of this itemset to one if the corresponding XML data exit in the dataset, zero for otherwise. Like Aprioi methods, the proposed efficient mining association rules with two steps: to find frequent itemsert and to generate possible association rules between XML data. Our proposed system EARM may reduce the memory storage size and it returns association rules with short response time.