Discovery of frequent itemsets is a very important data mining problem with numerous applications. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on frequent itemset mining has been done so far, focusing mainly on developing faster complete mining al...

We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long transactions (that is, transactions containing many items)...

Frequent itemset mining can be regarded as advanced database querying where a user specifies the dataset to be mined and constraints to be satisfied by the discovered itemsets. One of the research directions influenced by the above observation is the processing of sets of frequent itemset queries operating on overlapping datasets. Several methods of solving this problem have been proposed, all ...

In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent itemset mining process is represented as a data flow controlled by a series producer and consumer components (filters), and the data flow (communication) between such filters is made via streams. When production rate matches consup...

Frequent itemset mining can be regarded as advanced database querying where a user specifies constraints on the source dataset and patterns to be discovered. Since such frequent itemset queries can be submitted to the data mining system in batches, a natural question arises whether a batch of queries can be processed more efficiently than by executing each query individually. So far, two method...

The choices for mining of decentralized data are numerous, and we have developed techniques to enumerate and optimize decentralized frequent itemset counting. In this paper, we introduce our heuristic approach to improve the performance of such techniques developed in ways similar to query processing in database systems. We also describe empirical results that validate our heuristic techniques.

Recently, high utility pattern or itemset mining has become the most important research issues in data mining. In high utility itemset mining, the profit values for every item are considered. Generating high utility itemsets from a set of transactions in horizontal data format is a common practice. We hereby present the study of issues related to the different structures used and algorithms for...