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...

The disadvantages of apriori algorithm are firstly discussed. Then, a new measure of kendall-τ is proposed and treated as an interest threshold. Furthermore, an improved Apriori algorithm called K -apriori is proposed based on kendall-τ correlation coefficient. It not only can accurately find the relations between different products in transaction databases and reduce the useless rules but also...

The original Apriori algorithm is widely used in the intrusion detection field, but it may consume incredible computing resources in the process of handling network packets. We propose our optimized-Apriori algorithm which can greatly improves the algorithm efficiency by means of reducing the data storage space and the number of frequent item sets. We take full advantage of the characteristics ...

Association rule mining has attracted wide attention in both research and application area recently. Mining multilevel association rules is one of the most important branch of it. This paper introduces an improved apriori algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. New FP tree...

  The unprecedented growth of competition in the banking technology has raised the importance of retaining current customers and acquires new customers so that is important analyzing Customer behavior, which is base on bank databases. Analyzing bank databases for analyzing customer behavior is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily t...

The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LS...

Association rule mining concept is used to show relation between items in a set of items. Apriori algorithm for mining frequent itemsets from large amount of database is used. Parallelism is used to reduce time and increase performance, Multi-core processor is used for parallelization. Mining in a Serial manner can consume time and reduce performance for mining. To solve this issue we are propo...

In computer science and data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. The purpose of the Apriori Algorithm is to find associations between different sets of data. It is sometimes referred to as "Market Basket Analysis". The aim of this research is to improve the performance of the conventional Apr...