Semantic association rule mining: A new approach for stock market prediction

the amount of ontologies and semantic annotations available on the Web is constantly growing and heterogeneous data raises new challenges for the data mining community. Yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. In this paper, we survey some semantic data mining methods specifically focusing on association rules. However, there are few works that have focused in mining semantic web data itself. For extracting rules in semantic data, we present an intelligent data mining approach incorporated with domain. The paper contributes a new algorithm for discovery of new type of patterns from semantic data. This new type of patterns is appropriate for some data such as stock market. We take advantage of the knowledge encoded in the ontology and MICF measure to inference in three steps to prune the search space and generated rules to derive appropriate rules from thousands of rules. Some experiments performed on stock market data and show the usefulness and efficiency of the approach.