In general frequent itemsets are generated from large data sets by applying various association rule mining algorithms, these produce many redundant frequent itemsets. In this paper we proposed a new framework for Non-redundant frequent itemset generation using closed frequent itemsets without lose of information on Taxonomy Datasets using concept lattices. General Terms Frequent Pattern, Assoc...

In this paper, we propose a new method, namely EFI-Mine, for mining temporal emerging frequent itemsets from data streams efficiently and effectively. The temporal emerging frequent itemsets are those that are infrequent in the current time window of data stream but have high potential to become frequent in the subsequent time windows. Discovery of emerging frequent itemsets is an important pro...

Mining association rules is very popular in the data mining community. Most algorithms designed for finding association rules start with searching for frequent itemsets. Typically, in these algorithms, counting phases and pruning phases are interleaved. In the counting phase, partial information about the frequencies of selected itemsets is gathered. In the pruning phase as much as possible of ...

The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present ...

Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for mining frequent itemsets. In this paper, we propose DiffNodeset, a novel and more efficient itemset representation, for mining frequent itemsets...

Frequent closed itemsets is a complete and condensed representaion for all the frequent itemsets, and it’s important to generate non-redundant association rules. It has been studied extensively in data mining research, but most of them are done based on traditional transaction database environment and thus have performance issue under data stream environment. In this paper, a novel approach is ...

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a simple but effective algorithm for mining maximal frequent itemsets over a stream landmark. We design a compact data structure named FP-FOREST to improv...