Frequent itemsets mining finds sets of items that frequently appear together in a database. However, publishing this information might have privacy implications. Accordingly, in this paper we are considering the problem of guaranteeing differential privacy for frequent itemsets mining. We measure the utility of a frequent itemsets mining algorithm by its likelihood to produce a complete and sou...

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the...

This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when 316 C....

Efficient algorithms to discover frequent patterns are crucial in data mining research. Several effective data structures, such as two-dimensional arrays, graphs, trees, and tries have been proposed to collect candidate and frequent itemsets. It seems as the tree structure is most extractive to storing itemsets. The outstanding tree has been proposed so far is called FP-tree which is a prefix-t...

The discovery of association rules is an important task in data mining and knowledge discovery. Several algorithms have been developed for finding frequent itemsets and mining comprehensive association rules from the databases. The efficiency of these algorithms is a major issue since a long time and has captured the interest of a large community of researchers. This paper presents a new approa...

We propose a new on-line ε-approximation algorithm for mining closed itemsets from a transactional data stream, which is also based on the incremental/cumulative intersection principle. The proposed algorithm, called LC-CloStream, is constructed by integrating CloStream algorithm and Lossy Counting algorithm. We investigate some behaviors of the LC-CloStream algorithm. Firstly we show the incom...

This research aims at proposing a new method for discovering frequent temporal itemsets in continuous subsets of a dataset with quantitative transactions. It is important to note that although these temporal itemsets may have relatively high textit{support} or occurrence within particular time intervals, they do not necessarily get similar textit{support} across the whole dataset, which makes i...

Frequent itemset mining is a popular and important first step in the analysis of data arising in a broad range of applications. The traditional “exact” model for frequent itemsets requires that every item occurs in each supporting transaction. Real data is typically subject to noise and measurement error. To date, the effects of noise on exact frequent pattern mining algorithms have been addres...