The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected graph. This problem arises in applications such as predicting congestion in network traffic. An algorithm based on Apriori, called AFS, is developed, but with significantly improved efficiency through exploiting the underlying graph structure, which makes AFS feasible for practical input pat...

A Huge amount of data gets collected from society with different sources. Hardly has it led to a useful knowledge. For finding useful knowledge an algorithm is required. Apriori is an algorithm for mining data from databases which shows items that are related to each other. The databases having a size in GB and TB need a fast processor. For fast processing multicore processors are used. Paralle...

Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. Now Graphics Processing Unit (GPU) has taken a major role in high performance computing for generic applications. Compute Unified Device Architecture (CUDA) programming mo...

Association rule discovery plays an important role in knowledge discovery and data mining, and efficiency is especially crucial for an algorithm finding frequent itemsets from a large database. Many methods have been proposed to solve this problem. In addition, parallel computing has been a popular trend, such as on cloud platform, grid system or multicore platform. In this paper, a high effici...

In the field of data mining, classification and association set rules are two of very important techniques to find out new patterns. K-nearest neighbor and apriori algorithm are most usable methods of classification and association set rules respectively. However, individually they face few challenges, such as, time utilization and inefficiency for very large databases. The current paper attemp...

Investment in the related stocks in share market plays vital role for investors. Variation in stock price is the barometer of growth of companies/sectors. Association Rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets and predicted the associative and correlative behaviour for new data. In the p...

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...

We suggest the original procedure for frequent itemsets generation, which is more efficient than the appropriate procedure of the well known Apriori algorithm. The correctness of the procedure is based on a special structure called Rymon tree. For its implementation, we suggest a modified sort-merge-join algorithm. Finally, we explain how the support measure, which is used in Apriori algorithm,...

Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed ...

This paper investigates the problem of mining unconnected patterns in workflows and presents for its solution two algorithms, both adapting the Apriori approach to the graphical structure of workflows. The first one is a straightforward extension of the level-wise style of Apriori whereas the second one introduces sophisticated graphical analysis of the frequencies of workflow instances. The ex...