Mining numerical association rules via multi-objective genetic algorithms (original) (raw)
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Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules
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Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.
Association Rules Extraction using Multi-objective Feature of Genetic Algorithm
2013
Association Rule Mining is one of the most well - liked techniques of data mining strategies whose primary aim is to extract associations among sets of items or products in transactional databases. However, mining association rules typically ends up in a really large amount of found rules, leaving the database analyst with the task to go through all the association rules and find out the interesting ones. Currently Apriori Algorithm plays an important role in deriving frequent itemsets and then extracting association rules out of it. However Apriori Algorithm uses Conjunctive nature of association rules, and single minimum support threshold to get the interesting rules. But these factors don't seem to be alone sufficient to extract interesting association rules effectively. Hence in this paper, we proposed a completely unique approach for optimizing association rules using Multi-objective feature of Genetic Algorithm with multiple quality measures i.e. support, confidence, compr...
223 Review on Novel Genetic Algorithm for Association Rule Mining with Multi-Objective Extraction
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Nowadays, the advancement in the technology has led to the enormous growth of data generated in the digital form. This leads to the situation where to extract interesting and useful knowledge from this vast amount of data becomes an attractive and challenging task. To help the situation, Data Mining techniques can be used which extract the relevant information from a large amount of data by using predictive and descriptive models. Discovering Association Rules is one of the Data Mining Techniques that is widely used today for the purpose of, say, guessing the frequent buying patterns.The most popular algorithms used for this purpose are Apriori and FP-Growth algorithms, other methods simply inherit the properties of any of the two.This paper makes an insight over the approaches used for the purpose of Association Rule Mining to overcome the drawbacks of previous approaches by utilizing their important aspects. This will give rise to future research on Asociation Rule Mining and high...
A multi-objective evolutionary algorithm for mining quantitative association rules
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Abstract Data mining is most commonly used in attempts to induce association rules from database. Recently, some researchers have suggested the extraction of association rules as a multi-objective problem, removing some of the limitations of current approaches. In this way, we can jointly optimize quality measures which can present different degrees of tradeoff depending on the database used and the type of information can be extracted from it.
Expert Systems with …, 2011
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Multi-objective Association Rule Mining using Evolutionary Algorithm
International Journal of Advanced Research in Computer Science and Software Engineering, 2017
Generally association rule mining (ARM) algorithms, like the apriori algorithm, initial produce frequent itemsets and afterward, from the frequent itemsets, the association rules that go beyond the minimum confidence threshold. When the data is in large volume, it takes number of scans to generate frequent items.It is a better idea if all the association rules generated directly without generating frequent items and reduce number of scanning of the database. The quality of an association rule cannot only be signified by its support or confidence. There are numerous other metrics existing to determine the quality of an association rule. Then, the concept of ARM can be present as a multi-objective optimization problem in which the objective is to find association rules while optimizing a number of such goodness and quality criteria at the same time. This point of view, evolutionary algorithms have been utilizing extensively for producing association rules. In this paper, in-depth study on various objectives for ARM and some evolutionary algorithms has been done.
Analysis of Various Multiobjective Genetic Approaches in Association Rule Mining
International Journal of Computer Applications, 2014
Data mining is used now days by companies with a strong consumer focus. It enables these companies to know the relationships among "internal" factors such as, product positioning, price or staff skills, and "external" factors such as indicators, economic, competition, and customer demographics. The overall aim of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. In this paper, the multi-objective genetic approach for the result Comparison of Pittsburgh and Michigan approach using multi-objective genetic algorithm has been proposed, and it is shown that using Pittsburgh approach is much better than the Michigan approach.
Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm
Pattern Analysis and Applications, 2008
Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.
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.
2003
In this paper, we propose an automated clustering method based on multi-objective genetic algorithms (GA); the aim of this method is to automatically cluster values of a given quantitative attribute to obtain large number of large itemsets in low duration (time). We compare the proposed multi-objective GA-based approach with CURE-based approach. In addition to the autonomous specification of fuzzy sets, experimental results showed that the proposed automated clustering exhibits good performance over CURE-based approach in terms of runtime as well as the number of large itemsets and interesting association rules. ¤ },