Mimning Algorithm For Temporal Text Association Rules In Text Mining

With the growth in the databases of text, which is temporal and frequently updated, corresponding temporal association rules should be generated for the text database and the hidden information be dug out. Although the association rules algorithm has been intensively and extensively studied, the temporal association rule algorithm in text data is still unusual.In this paper, we propose a temporal association rule algorithm to study the temporal text. Firstly, we preprocess the temporal text, using the vector space to represent the database. Then, develop the model for the temporal text and the temporal text association rules. And then, we propose the temporal text association rule algorithm:SPFM. Finally, we verify the validation of the algorithm by an experiment.This paper applies the SPFM algorithm into mining temporal text association rules use by C++programming language. We convert the temporal text database into vertical data format, then use SPFM algorithm to find out effective time, take out the orresponding frequent itemsets. Finally, we demonstrate that the SPFM algorithm is practical and realistic. This algorithm has great significance in the practical application. It can not only be used in medical virus thesis, but also can be used in most of the computer virus mining, police mining and so on in temporal text database. We will combine temporal data and text mining in the maturing background of text data mining technology, and use temporal text data mining in a variety of text database. It will make a contribution to the research work in the future.