The first step of association rule mining is finding out all frequent itemsets. Generation of reliable association rules are based on all frequent itemsets found in the first step. Obtaining all frequent itemsets in a large database leads the overall performance in the association rule mining. In this paper, an efficient method for discovering the maximal frequent itemsets is proposed. This met...

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present e cient algorithms for the discovery of frequent itemsets, which forms the compute intensive phase of the task. The algorithms utilize ...

Since its introduction, frequent itemset mining has been the subject of numerous studies. However, most of them return frequent itemsets in the form of textual lists. The common cliché that “a picture is worth a thousand words” advocates that visual representation can enhance user understanding of the inherent relations in a collection of objects such as frequent itemsets. Many visualization sy...

A parallel algorithm for finding the frequent itemsets in a set of transactions is presented. The frequent individual items are identified by their index. We assume that processors number (m) is less than the frequent items number (n). At the first stage, every processor Pi, i ∈ {1, . . . ,m − 1} sequentially computes the frequent itemsets from the interval Ii = [(i − 1) · p + 1, i · p], where ...

Data Mining can be delineated as an action that analyze the data and draws out some new nontrivial information from the large amount of databases. Traditional data mining methods have focused on finding the statistical correlations between the items that are frequently appearing in the database. High utility itemset mining is an area of research where utility based mining is a descriptive type ...

Prediction in shopping cart uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. In order to reduce the rule mining cost, a fast algorithm generating frequent itemsets without generating candidate itemsets is proposed. The algorithm uses Boolean vector with relational AND operation to discover frequent itemsets and generat...