Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets (original) (raw)

Mining Most Generalization Association Rules Based on Frequent Closed Itemset +

2012

Association rule mining plays an important role in knowledge discovery and data mining. The rules obtained by some previous works based on support and confidence measures might be redundant to a certain degree. This paper thus proposes the concept of most generalization association rules (MGARs), which are more compact than the three previous rule types that include traditional association rules, non-redundant association rules and minimal non-redundant association rules. Some theorems relating to the properties of MGARs are derived as well, and an algorithm based on the theorems for effectively pruning unpromising rules early is then proposed. Hash tables are used to check whether the generated rules are redundant or not. Experimental results show that the number of MGARs generated from a database is much smaller than that of nonredundant association rules and that of minimal non-redundant association rules.

Discovering Frequent Closed Itemsets for Association Rules

1999

In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice rather than the subset lattice. Moreover, we show that the set of all frequent closed itemsets suffices to determine a reduced set of association rules, thus addressing another important data mining problem: limiting the number of rules produced without information loss.We propose a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets. We realized experiments to compare our approach to the commonly used frequent itemset search approach. Those experiments showed that our approach is very valuable for dense and/or correlated data that represent an important part of existing databases.

Mining Association Rules from Infrequent Itemsets: A Survey

International Journal of Innovative Research in Science, Engineering and Technology, 2013

Association Rule Mining (AM) is one of the most popular data mining techniques. Association rule mining generates a large number of rules based on support and confidence. However, post analysis is required to obtain interesting rules as many of the generated rules are useless.However, the size of the database can be very large. It is very time consuming to find all the association rules from a large database, and users may be only interested in the associations among some items.So mining association rules in such a way that we maximize the occurrences of useful pattern. In this paper we study several aspects in this direction and analyze the previous research.So that we come with the advantages and disadvantages.

A Survey on Association Rule Mining Algorithms for Frequent Itemsets

— These days many current data mining tasks are accomplished successfully only in discovery of Association rule. It appeals more attention in frequent pattern mining because of its wide applicability. Many researchers successfully presented several efficient algorithms with its performances in the area of rule generation. This paper mainly assembles a theoretical survey of the existing algorithms. Here author provides the considered Association rule mining algorithms by beginning an overview of some of the latest research works done on this area. Finally, discusses and concludes the merits and limitation.