There is a huge amount of data around us and to extract valuable information from it can be done through data mining. Data Miming is the process of extracting useful information from the huge amount of data stored in databases. Association Rule Mining is one of the data mining techniques used to extract hidden knowledge from datasets that can be used by an organization’s decision makers to impr...

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...

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...

In this paper, the relation among factors in the road transportation sector from March, 2005 to March, 2011 is analyzed. Most of the previous studies have economical point of view on gasoline consumption. Here, a new approach is proposed in which different data mining techniques are used to extract meaningful relations between the aforementioned factors. The main and dependent factor is gasolin...

With the unceasing development of information and technology in today’s modern society, enterprises’ demand of human resources information mining is getting bigger and bigger. Based on the enterprise human resources information mining situation, this paper puts forward a kind of improved Apriori algorithm based model on the enterprise human resources information mining, this model introduced da...

The popular association rule algorithms are Apriori and fp-growth; both of these very familiar among data mining researchers; however, there some weaknesses found in the algorithm, including long dataset scans process finding frequency item set, using large memory, resulting rules being sometimes less than optimal. In this study, authors made a comparison fp-growth, Apriori, TPQ-Apriori to anal...

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 and vehicular traffic. An algorithm, called AFS, based on the classic frequent itemset mining algorithm Apriori is developed, but with significantly improved efficiency over Apriori from exponential in transa...

This paper presents a variation of Apriori algorithm that includes the role of domain expert to guide and speed up the overall knowledge discovery task. Usually, the user is interested in finding relationships between certain attributes instead of the whole dataset. Moreover, he can help the mining algorithm to select the target database which in turn takes less time to find the desired associa...