Complete Set Of Association Rules Research Articles

Mining Several Kinds of Multi-Dimensional Temporal Association Rule

To improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Previously proposed methods are mainly contributed to handling a single constraint and only consider the constraints which are characterized by a single attribute value. In this paper, we propose an approach to mine association rules with multiple constraints constructed by multi-dimensional attribute values. Our proposed approach basically consists of three phases. First, we collect the frequent items and prune infrequent items according to the Apriority property. Second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each item set possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Our proposed algorithms can exploit the properties of constraints to prune search space or save constraint checking. Therefore, our proposed algorithm is more efficient than the revised FP (Frequent Pattern)-growth and FIC (Fractal Image Compression) algorithms.

An efficient method for mining association rules based on minimum single constraints

Mining association rules with constraints allow us to concentrate on discovering a useful subset instead of the complete set of association rules. With the aim of satisfying the needs of users and improving the efficiency and effectiveness of mining task, many various constraints and mining algorithms have been proposed. In practice, finding rules regarding specific itemsets is of interest. Thus, this paper considers the problem of mining association rules whose left-hand and right-hand sides contain two given itemsets, respectively. In addition, they also have to satisfy two given maximum support and confidence constraints. Applying previous algorithms to solve this problem may encounter disadvantages, such as the generation of many redundant candidates, time-consuming constraint check and the repeated reading of the database when the constraints are changed. The paper proposes an equivalence relation using the closure of itemset to partition the solution set into disjoint equivalence classes and a new, efficient representation of the rules in each class based on the lattice of closed itemsets and their generators. The paper also develops a new algorithm, called MAR-MINSC, to rapidly mine all constrained rules from the lattice instead of mining them directly from the database. Theoretical results are proven to be reliable. Because MAR-MINSC does not meet drawbacks above, in extensive experiments on many databases it obtains the outstanding performance in comparison with some of existing algorithms in mining association rules with the constraints mentioned.

Evolutionary selection of interesting class association rules using genetic relation algorithm

AbstractDuring the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Association Rules Mining with Multiple Constraints

Association rules mining(ARM) is an important task in the field of data mining, mining frequent itemsets is a key step of many algorithms for ARM. In a very large dataset, rules generated may be very large, but some of them are useless to the users, to improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Most of previously proposed methods are mainly deal with a single constraint. In this paper, we present an algorithm for mining association rules with multiple constraints, the proposed algorithm simultaneously copes with two different kinds of constraints, it consists of three phases, first, the frequent 1-itemset are generated, second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each itemset possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Experimental results show that the proposed method outperform the revised FP-growth algorithm.

Constraint-based Web Log Mining for Analyzing Customers’ Behaviour

Analysis of Web logs is one of the important challenges to provide Web intelligent services. Association rule mining algorithms are used widely to track users' web behaviour. Due to large amount of data many times the rules formed by these algorithms are very long and redundant. Recently Constraintbased mining approaches have received attention to deal with these big and redundant association rules. In this paper we discuss the Constraint based web mining approach used to reduce the size of association rules derived from Web log. The approach proves effective in reducing the overlap of information and also improves the efficiency of mining tasks. Constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules.

Mining association rules with multi-dimensional constraints

To improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Previously proposed methods are mainly contributed to handling a single constraint and only consider the constraints which are characterized by a single attribute value. In this paper, we propose an approach to mine association rules with multiple constraints constructed by multi-dimensional attribute values. Our proposed approach basically consists of three phases. First, we collect the frequent items and prune infrequent items according to the Apriori property. Second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each itemset possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Our proposed algorithms can exploit the properties of constraints to prune search space or save constraint checking. Therefore, our proposed algorithm is more efficient than the revised FP-growth and FIC algorithms.

Mining association rule efficiently based on data warehouse

The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.