Frequent Items Mining on Data Stream Based on Weighted Counts

Abstract

Frequent items mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent items mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called count-MH to discover weighted frequent items over data streams, the proposed method is based on weighted factor and hash function where its space complexity is, the processing time for each item is in average. Experimental results show that count-MH is efficient for frequent items mining.