Sharif Digital Repository

Recently, there has been a growing attention in frequent itemset mining in distributed systems. In this paper, we present an algorithm to extract frequent itemsets from large distributed datasets. Our algorithm uses gossip as the communication mechanism and does not rely on any central node. In gossip based communication, nodes repeatedly select other random nodes in the system, and exchange information with them. Our algorithm proceeds in rounds and provides all nodes with the required support counts of itemsets, such that each node is able to extract the global frequent itemsets. For local iteration and generation of candidate itemsets, a trie data structure is used, which facilitates the...