Data mining is a widely used technology for various real-life applications of data analytics and important to discover valuable association rules in transaction databases. Interesting itemset plays an role many applications, such as market, e-commerce, finance, medical treatment. To date, algorithms based on frequent patterns have been studied, but there are few that focus infrequent or rare pa...

Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset Mining, which discovers itemsets that occur frequently (i.e. with occurrence count larger than a user given value). The problem of finding High Utility Itemsets is challenging, because the anti-monotone property so use...

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...

In this paper, we propose a parallel algorithm for mining maximal itemsets. We propose POP-MAX (Parallel Order Preserving MAXimal itemset algorithm), a fast and memory efficient parallel algorithm which enumerates all the maximal patterns concurrently and independently across several nodes. Also, POP-MAX uses an efficient maximality checking technique which determines the maximality of an items...

The market basket is defined as an itemset bought together by a customer on a single visit to a store. The market basket analysis is a powerful tool for the implementation of cross-selling strategies. Especially in retailing it is essential to discover large baskets, since it deals with thousands of items. Although some algorithms can find large itemsets, they can be inefficient in terms of com...

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...