One of the most important data mining problems is mining association rules. In this paper we consider discovering association rules from large transaction databases. The problem of discovering association rules can be decomposed into two sub-problems: find large itemsets and generate association rules from large itemsets. The second sub-problem is easier one and the complexity of discovering as...

Association rule mining is the most important technique in the field of data mining. The main task of association rule mining is to mine association rules by using minimum support thresholds decided by the user, to find the frequent patterns. Above all, most important is research on increment association rules mining. The Apriori algorithm is a classical algorithm in mining association rules. T...

Association Rule mining is one of the important and most popular data mining techniques. It extracts interesting correlations, frequent patterns and associations among sets of items in the transaction databases or other data repositories. Apriori algorithm is an influential algorithm for mining frequent itemsets for Boolean association rules. Firstly, the concept of association rules is introdu...

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...

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...

Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suuers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemse...

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...

Data mining is a field which searches for interesting knowledge or information from existing massive collection of data. In particular, algorithms like Apriori help a researcher to understand the potential knowledge, deep inside the data base. But due to the large time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorithm. In this ...

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...