Matrix-based Algorithm for Predicting Frequent Patterns over Data Streams

With the wide application of data mining, many practical data mining applications need to use past and current data to predict the future state of the data.To solve this problem, we propose a new method (MFP) for predicting frequent patterns over data streams.MFP algorithm can predict those frequent itemsets that have high potential to become frequent in the subsequent time windows, to meet users' needs.Firstly, the algorithm converts the data to 0-1 matrix.Then it will update the matrix by tailoring it and bit operations, from which mine frequent itemsets as well.Finally, it will predict possible frequent itemsets that may appear in the next time window by using the current data.Experimental results show that MFP algorithm can predict the frequent itemsets in different experimental conditions, therefore, the algorithm is feasible.