4,121 research outputs found
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.
Generating a condensed representation for association rules
International audienceAssociation rule extraction from operational datasets often produces several tens of thousands, and even millions, of association rules. Moreover, many of these rules are redundant and thus useless. Using a semantic based on the closure of the Galois connection, we define a condensed representation for association rules. This representation is characterized by frequent closed itemsets and their generators. It contains the non-redundant association rules having minimal antecedent and maximal consequent, called min-max association rules. We think that these rules are the most relevant since they are the most general non-redundant association rules. Furthermore, this representation is a basis, i.e., a generating set for all association rules, their supports and their confidences, and all of them can be retrieved needless accessing the data. We introduce algorithms for extracting this basis and for reconstructing all association rules. Results of experiments carried out on real datasets show the usefulness of this approach. In order to generate this basis when an algorithm for extracting frequent itemsetsâsuch as Apriori for instanceâis used, we also present an algorithm for deriving frequent closed itemsets and their generators from frequent itemsets without using the dataset
FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases
In recent years, discovery of association rules among itemsets in a large
database has been described as an important database-mining problem. The
problem of discovering association rules has received considerable research
attention and several algorithms for mining frequent itemsets have been
developed. Many algorithms have been proposed to discover rules at single
concept level. However, mining association rules at multiple concept levels may
lead to the discovery of more specific and concrete knowledge from data. The
discovery of multiple level association rules is very much useful in many
applications. In most of the studies for multiple level association rule
mining, the database is scanned repeatedly which affects the efficiency of
mining process. In this research paper, a new method for discovering multilevel
association rules is proposed. It is based on FP-tree structure and uses
cooccurrence frequent item tree to find frequent items in multilevel concept
hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
Analyze Market Basket Data using FP-growth and Apriori Algorithm
In this paper we find the association rules among the large dataset. To find association rules we use two algorithm i.e.e.e. FP - growth and Apriori algorithms. First we find frequent itemsets using Weka tool and Rapid - miner tool. Then we generate association rules from the frequent itemsets. We have analyzed that as per this research FP - tree much faster than Apriori algorithm to generate association rules when we use large dat aset
Closed Association Rules
In this paper we present a new basis for association rules called Closed Association Rules (CR). This basis contains all valid association rules that can be generated from frequent closed itemsets. CR is a lossless representation of all association rules. Regarding the number of rules, our basis is between all association rules (AR) and minimal non-redundant association rules (MNR), filling a gap between them. The new basis provides a framework for some other bases and we show that MNR is a subset of CR. Our experiments show that CR is a good alternative for all association rules. The number of generated rules can be much less, and beside frequent closed itemsets nothing else is required
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