This paper presents two new ways of example weighting for subgroup discovery. The proposed example weighting schemes are applicable to any subgroup discovery algorithm that uses the weighted covering approach to discover interesting subgroups in data. To show the implications that the new example weighting schemes have on subgroup discovery, they were implemented in the APRIORI-SD algorithm. RO...

Identification and characterization of gene regulatory binding motifs is one of the fundamental tasks toward systematically understanding the molecular mechanisms of transcriptional regulation. Recently, the problem has been abstracted as the challenge planted (l,d)-motif problem. Previous studies have developed numerous methods to solve the problem. But most of them need to specify the length ...

| Discovering association rules is one of the most important task in data mining. Many eecient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an eecient algorithm, called Close, based on a new...

The main aim is to generate a frequent itemset. Big Data analytics is the process of examining big data to uncover hidden patterns. Association Rule Learning is a technique which is used to implement big data. It finds the frequent items in the dataset. Frequent itemsets are those items which occur frequently in the database. To find the frequent itemsets, we are using three algorithms APRIORI ...

Cloud <span>computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because this factor cloud computing is emerging as in primary data storage for individuals well organisations. At the same time, privacy preservation an also a significant aspect computing. In regrades to preservation, association rule mining was proposed...

Most algorithms for association rule mining are variants of the basic Apriori algorithm One characteristic of these Apriori based algorithms is that candidate itemsets are generated in rounds with the size of the itemsets incremented by one per round The number of database scans required by Apriori based algorithms thus depends on the size of the largest large itemsets In this paper we devise a...

Data Mining techniques are helpful to uncover the hidden predictive patterns from large masses of data. Frequent item set mining also called Market Basket Analysis is one the most famous and widely used data mining technique for finding most recurrent itemsets in large sized transactional databases. Many methods are devised by researchers in this field to carry out this task, some of these are ...