Associative Feature Selection for Text Mining

With the exponential growth of the number of documents available on the Internet, automatic feature selection approaches are increasingly important for the preprocessing of textual documents for data mining. Feature selection, which focuses on identifying relevant data, can help reduce the workload of processing huge amounts of data as well as increase the accuracy for the subsequent data mining tasks. In this paper, we propose a new feature selection approach for text mining based on association rules. An evaluation on the performance of the proposed associative feature selection approach based on a dataset of published data mining papers is also presented.