In the growing concerns about data privacy and increasingly stringent data security regulations, it is not feasible to directly mine data or share data if the dataset contains private data. Collecting and analyzing data from multiple parties ...

With the development of the Internet, a large amount of transaction data (e.g., shopping records, web browsing history), which represents user data, has been generated. By collecting user transaction data and learning specific patterns and association ...

In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on data patterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding ...

A number of studies and methods have been proposed to obtain more efficient patterns that meet the requirements of decision makers by considering frequency and utility thresholds, for example, frequent high utility patterns and high utility rare ...

In data mining, finding frequent itemsets is a critical step to discovering association rules. The number of frequent itemsets may, however, be huge if the threshold of minimum support is set at a low value or the number of items in the ...

Most Algorithms for frequent item set mining typically make the assumption that data is centralized or static. They may waste computational and I/O resources when the data is dynamic, and they impose excessive communication overhead when the data is ...

Nowadays, frequent item set mining is the major task in association rule mining. With the observation that the support plays an important role in mining frequent item sets, in this paper, we review the previous efficient algorithms and study the effect ...

Frequent itemset mining is a core data mining task and has been studied extensively. Although by their nature, frequent itemsets are aggregates over many individuals and would not seem to pose a privacy threat, an attacker with strong background ...

Frequent item set mining is a fundamental element with respect to many data mining problems. Recently, the PrePost algorithm has been proposed, a new algorithm for mining frequent item sets based on the idea of N-lists. PrePost in most cases outperforms ...

Association Rule Mining becomes a vast area of research in last few decades. The basic idea behind ARM is to mine positive (interesting) and negative (uninteresting) rules from a transaction database. In this paper we have proposed a new model for ...

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number ...

In the past, Hong et al. proposed an algorithm to maintain the fast updated frequent pattern tree (FUFP-tree), which was an efficient data structure for association-rule mining. However in the maintenance process, the counts of infrequent items and the ...

In this paper, we present an incremental mining algorithm for handling the mining problem from inserted transactions. The algorithm is based on the Dynamic Bit-Vector (DBV) structure and pre-large item sets. The DBV structure facilitates the processes ...

The association rule mining and its usages have thrown the lights to the different possibilities for the researchers. The importance of association rule mining is getting increased day by day due to the proliferation of internet as well as the fiercer ...

In order to analyze the shopping habits of consumers and more accurately mine the characteristics of consumption, we hereby have proposed and proved Theorem 1-3 to improve the classical Apriori algorithm, resulting in the reduction of database access. ...

The fast updated frequent pattern tree (FUFP-tree) is an efficient data structure for association-rule mining. Hong et al. (2009) proposed an approach for the maintenance of the FUFP-tree structure after the deletion of transactions. However, all ...

Efficient mining of frequent itemsets from a database plays an essential role in many data mining tasks such as association rule mining. Many algorithms use a prefix-tree to represent a database and mine frequent itemsets by constructing recursively ...

Recently, high utility itemsets mining becomes one of the most important research issues in data mining due to its ability to consider different profit values for every item. In the past studies, most algorithms generate high utility itemsets from a set ...

Many projects have tried to analyze the structure and dynamics of application overlay networks on the Internet using packet analysis and network flow data. While such analysis is essential for a variety of network management and security tasks, it is ...

Mining high utility item sets from transactional databases is an important data mining task, which refers to the discovery of item sets with high utilities (e.g. high profits). Although several studies have been carried out, current methods may present ...