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...

In this paper, we present PRICES, an efficient algorithm for mining association rules, which first identifies all large itemsets and then generates association rules. Our approach reduces large itemset generation time, known to be the most time-consuming step, by scanning the database only once and using logical operations in the process. Experimental results and comparisons with the state of t...

In this paper, we propose a ubiquitous product rating system. First we provide an overview of state-of-the-art product recommendation, concluding that current approaches do not support in-store consumers; i.e. consumers on the shop floor. Within shops however is where three out of four buying decisions are made. Hence we propose APriori, a new approach towards mobile product recommendation. APr...

A great research work has been done in last decade in association rules mining (ARM) algorithms . Therefore, various algorithms were proposed to discover frequent item sets and then mine association rules. Apriori algorithm is the most frequently used algorithm for generating association rules. Apriori algorithm has some abuses, such as too many scans of the database, large load of system’s I/O...