Frequent itemset mining with elimination of null transactions over data streams

Abstract

A data stream is an input massive data that arrives at high speed and it is unbounded. The sliding window model is used to extract the recent frequent patterns by adjusting the window size containing only the recent transactions and eliminating the old transactions. Another acute challenge in frequent pattern mining is the presence of null transactions. Null transaction is a transaction which contains only a single item and its presence does not contribute toward frequent pattern discovery. Most of the existing streaming algorithms did not consider the overhead of null transactions, and hence, they fails to discover the frequent patterns faster during mining process. To overcome these issues, a new algorithm called frequent itemset mining using variable size sliding window with elimination of null transactions (FIM-VSSW-ENT) is used for extracting recent frequent patterns from data streams. Experimental results using synthetic and real datasets show that our proposed algorithm gives better result in terms of processing time and memory storage.