Credit scoring is an important topic and banks collect different data from their loan applicants to make appropriate and correct decisions. Rule bases are favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants. This paper, uses four feature selection approaches as features pre-processing combined with fuzzy apriori. These methods a...

It is an important part of research content in data mining to discover association rules from large scale database, the main problem of which is frequent itemsets mining. The classical Apriori algorithm is an efficient one for that. Aimed at the performance bottlenecks of multiply scanning the database and generating a large quantity of candidate itemsets in Apriori algorithm, an improved algor...

Association rules have been developed for years and applied successfully for market basket analysis and cross selling among other business applications. One of the most used approaches in association rules is the Apriori algorithm. However the Apriori algorithm, has long known for its weaknesses that generate enormous amount of rules and alreadyknown facts. In this study, we integrate the RFM a...

This work discusses the problem of generating association rules from a set of transactions in a relational database, taking performance and accuracy of found results as the essential aspects for comparing association mining algorithms. We do a critical analysis of two previously existing methods, Apriori and FP-growth, emphasizing their strengths and weaknesses; and based on this analysis, we p...

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...

Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. Mining frequent patterns from large scale databases has emerged as an important problem in data mining and knowledge discovery community. A number of algorithms has been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the...

In data mining frequent item-sets mining is one of the important tasks. Apriori is used to mine the frequent item-sets but, Apriori also has some problem as in Apriori finding of support count is very time consuming procedure. To overcome this problem of Apriori, BitApriori algorithm is proposed for mining frequent item-sets, but the BitApriori also suffer the problem of memory scarcity when th...

Data mining is wide spreading its applications in several areas. There are different tasks in mining which provides solutions for wide variety of problems in order to discover knowledge. Among those tasks association mining plays a pivotal role for identifying frequent patterns. Among the available association mining algorithms Apriori algorithm is one of the most prevalent and dominant algorit...

Association rule mining is an important data mining problem. Since its inception, different variants of rules has been proposed in the literature. In each case, different attributes (e.g., weight and quantity) are considered to obtain more informative rules. To our knowledge, each proposal is based on the Apriori algorithm that is, in modern context, inefficient. Methods that outperform the Apr...

Abstract— Data mining is a field which explores for exciting knowledge or information from existing substantial group of data. In particular, algorithms like Apriori aid a researcher to understand the potential knowledge, deep inside the database. However because of the huge time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorith...