A novel approach for mining emerging patterns in data streams

Streaming data mining is one of the most difficult tasks in knowledge discovery in databases (KDD). This task is essential in many applications such as financial applications, network monitoring, marketing and others. In this model, data arrives in multiple, continuous, rapid, time-varying data streams. These characteristics make it infeasible for traditional mining techniques to deal with data streams. In this paper, we propose a new approach for mining emerging patterns [EPs] in streaming data. EPs are those itemsets whose frequencies in one class are significantly higher than their frequencies in the other classes. We experimentally prove that our new method for mining EPs has an excellent impact on the process of classifying data streams.