On the discovery of association rules by means of evolutionary algorithms (original) (raw)

An Evolutionary Algorithm for Mining Association Rules Using Boolean Approach

Frequent pattern mining is one of the active research themes in data mining. It plays an important role in all data mining tasks such as clustering, classification, prediction, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of patterns generated. A reasonable solution is identifying efficient method to finding frequent patterns without candidate generation. In this paper, we present An Evolutionary algorithm for mining association rules using Boolean approach for mining association rules in large databases of sales transactions. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm is proposed in this paper. This new algorithm scanning the database once and avoiding generating candidate itemsets in computing frequent itemset and adopts a Boolean vector "relational calculus" method to discovering frequent itemsets. Experimental results show that this algorithm can quickly discover frequent itemsets and effectively mine potential association rules.

A Survey of Genetic Algorithm for Association Rule Mining

International Journal of Computer Applications, 2013

In recent years, Data Mining is an important aspect for generating association rules among the large number of itemsets. Association Rule Mining is the method for discovering interesting relations between variables in large databases. It is considered as one of the important tasks of data mining intended towards decision making. Genetic algorithm (GA) based on evolution principles has found its strong base in mining Association Rules. Genetic algorithm is a search heuristic which is used to generate useful solutions to optimization and search problems. Genetic algorithm has proved to generate more accurate results when compared to other formal methods available. The fitness function used in Genetic Algorithm evaluates the quality of each rule. Many researchers have proposed genetic algorithm for mining interesting rules from dataset. This paper presents the survey of Genetic Algorithm for Association Rule Mining.

A Genetic Algorithm to Optimize Association Rules

Data mining is synonymous with knowledge mining which means extraction of useful information from an existing dataset and transforms it into a flexible structure. Association rule mining is one of the most important tasks of data mining. It is the process of finding some relations among the attribute values of a large database. Genetic algorithms have found their strong base in mining Association Rules. Many researchers have proposed genetic algorithms for mining interesting rules from dataset. This paper provides an algorithm to optimized association rule using genetic algorithm.

OPTIMIZE ASSOCIATION RULES USING EVOLUTIONARY ALGORITHM GENERATED BY APRIORI ALGORITHM

– Association rule mining may be a crucial and area of the data mining methodology. It aims at finding some hidden information or relationships among the attributes of the data. Association rule mining may be a stimulating area of data mining analysis that discovers correlations between wholly different item sets throughout event information. To enhance the efficiency and accuracy of the proposed work this is incorporating additional measures in simple Genetic Algorithmic rule for such data processing issues. We have used Genetic algorithmic rule to optimize the generated rules and take fitness operate for the improvement and realize the optimum solutions. To compare these two implementations on the generation and fitness they needed in proposed work.

Discovering numeric association rules via evolutionary algorithm

2002

Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there are many efficient techniques to obtain these rules, although most of them require that the values of the attributes be discrete. To solve this problem, these techniques discretize the numeric attributes, but this implies a loss of information. In a general way, these techniques work in two phases: in the first one they try to find the sets of attributes that are, with a determined frequency, within the database (frequent itemsets), and in the second one, they extract the association rules departing from these sets. In this paper we present a technique to find the frequent itemsets in numeric databases without needing to discretize the attributes. We use an evolutionary algorithm to find the intervals of each attribute that conforms a frequent itemset. The evaluation function itself will be the one that decide the amplitude of these intervals. Finally, we evaluate the tool with synthetic and real databases to check the efficiency of our algorithm.

Evolutionary extraction of association rules: A preliminary study on their effectiveness

2009

Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transactions, however the data in real-world applications usually consists of quantitative values. In the last few years, many researchers have proposed Evolutionary Algorithms for mining interesting association rules from quantitative data. In this paper, we present a preliminary study on the evolutionary extraction of quantitative association rules.

An evolutionary algorithm to discover numeric association rules

Proceedings of the 2002 ACM symposium on Applied computing - SAC '02, 2002

Evolutionary selection of interesting class association rules using genetic relation algorithm

IEEJ Transactions on Electrical and Electronic Engineering, 2011

During 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.

Association Rule Identification and Optimization using Genetic Algorithm

International Journal for Scientific Research and Development, 2015

Data mining is the analysis step of the "Knowledge Discovery in Databases", It is the process that results in the detection of new patterns in large data sets. The main aim of data mining is to pull out knowledge from an existing dataset and transform it into a flexible structure. In data mining association rule is a popular and easy method to find frequent itemsets from large datasets. In general frequent itemsets are generated from large data sets by applying association rule mining take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the results of association rule mining. Our main purpose is by Genetic Algorithm to generate high quality Association Rules, by which we can get four data qualities like accuracy, comprehensibility, interestingness and completeness. Genetic Algorithms are powerful and widely applicable stochastic search and optimization methods based on the concepts of natural selection and natural evaluation. The advantage of using genetic algorithm is to discover high level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm. In this paper we are using the large dataset and our Experimental results on this dataset show the effectiveness of our approach. This paper provides the major improvement in the approaches for association rule mining using genetic algorithms.

Peculiarly Approaches for Association Rule Mining using Genetic Algorithm

2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2018

Association rule mining is an important approach to data mining. It extracts useful and hidden information. There are two methodologies to explore the association rules. One method is generating frequent pattern generation through apriori like algorithms whereas another methodology is by using the soft computing techniques especially genetic algorithm. Two important aspect which is most of the time unaddressed, is incremental data and multi-objective. Very few research work on incremental and multi-objective association rule mining has been done. This paper comprises of a comprehensive study of incremental data mining and a distinct study of genetic algorithms. It is observed that soft-computing technique perform better for association rules. There is also a need for Incremental algorithms which work better in the state of addition, deletion and modification of data. It is also found that strong need of Multi-objective Incremental association rule mining algorithm.

On the discovery of association rules by means of evolutionary algorithms (original) (raw)

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