High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...
In recent years, mining frequent itemsets over uncertain data has attracted much attention in the data mining community. Unlike the corresponding problem in deterministic data, th...
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to han...
Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an associatio...
In recent years, various constrained frequent pattern mining problem formulations and associated algorithms have been developed that enable the user to specify various itemsetbase...