Frequent pattern mining is main step in association rule mining. Several algorithms have been proposed for this, but the majority of these algorithms have two main problems that is large number of database scan and generating large candidate itemsets. This process is time intense because these algorithms first mine the minimal frequent itemsets and then generate maximal frequent itemsets from m...

In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks. Keywords—Datamining, Frequent itemsets, Frequent closed itemsets,...

In a mobile business collaboration environment, frequent itemsets analysis will discover the noticeable associated events and data to provide important information of user behaviors. Many algorithms have been proposed for mining frequent itemsets over data streams. However, in many practical situations where the data arrival rate is very high, continuous mining the data sets within a sliding wi...

Mining frequent itemsets in databases is an important and widely studied problem in data mining research. The problem of mining frequent itemsets is usually solved by constructing candidates of itemsets, and identifying those itemsets that meet the requirement of frequent itemsets. This paper proposes a novel algorithm based on BitTable (or bitmap) representation of the data. Data related to fr...