16,554 research outputs found
Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm
A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
Association rule mining is a well-known methodology to discover significant and apparently hidden relations among
attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association
rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use
of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data
nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on
genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new
representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve
successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary
algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have
been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies
have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The
comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of
computational costs without losing quality in the results when the proposed techniques are applied.Ministerio de Ciencia y Tecnología TIN2011- 28956-C02-02Junta de Andalucía TIC-7528Junta de Andalucía P12-TIC-1728Universidad Pablo de Olavide APPB81309
Temporal fuzzy association rule mining with 2-tuple linguistic representation
This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules
Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules
In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved
Discovering gene association networks by multi-objective evolutionary quantitative association rules
In the last decade, the interest in microarray technology has exponentially increased due to its
ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene
association networks from gene expression profiles is a relevant task and several statistical
techniques have been proposed to build them. The problem lies in the process to discover which
genes are more relevant and to identify the direct regulatory relationships among them. We
developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal
with this problem. We applied our methodology named GarNet to a well-known microarray data of
yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the
study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms
of quality metrics of the networks, such as accuracy and precision, which were measured using
YeastNet database as true network. Furthermore, the results were consistent with previous
biological knowledge.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752
QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules
The need to prediscretize numeric attributes before they can be used in
association rule learning is a source of inefficiencies in the resulting
classifier. This paper describes several new rule tuning steps aiming to
recover information lost in the discretization of numeric (quantitative)
attributes, and a new rule pruning strategy, which further reduces the size of
the classification models. We demonstrate the effectiveness of the proposed
methods on postoptimization of models generated by three state-of-the-art
association rule classification algorithms: Classification based on
Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016),
and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from
the UCI repository show that the postoptimized models are consistently smaller
-- typically by about 50% -- and have better classification performance on most
datasets
Class Association Rules Mining based Rough Set Method
This paper investigates the mining of class association rules with rough set
approach. In data mining, an association occurs between two set of elements
when one element set happen together with another. A class association rule set
(CARs) is a subset of association rules with classes specified as their
consequences. We present an efficient algorithm for mining the finest class
rule set inspired form Apriori algorithm, where the support and confidence are
computed based on the elementary set of lower approximation included in the
property of rough set theory. Our proposed approach has been shown very
effective, where the rough set approach for class association discovery is much
simpler than the classic association method.Comment: 10 pages, 2 figure
Analysis of Measures of Quantitative Association Rules
This paper presents the analysis of relationships among different
interestingness measures of quality of association rules as first step
to select the best objectives in order to develop a multi-objective algorithm.
For this purpose, the discovering of association rules is based on
evolutionary techniques. Specifically, a genetic algorithm has been used
in order to mine quantitative association rules and determine the intervals
on the attributes without discretizing the data before. The algorithm
has been applied in real-word climatological datasets based on Ozone and
Earthquake data.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261
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