An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization

Pachón, Victoria; Mata, Jacinto

doi:10.1016/j.eswa.2011.07.049

Cited by 32 publications

(5 citation statements)

References 8 publications

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“…Given that the above-mentioned metric tends to favor rules involving low number of attributes (Pachón Álvarez, V., et. al., 2012), in this work we consider as a further search objective the number of alarms involved in the association rules:…”

Section: Letmentioning

confidence: 99%

Multi-Objective Evolutionary Algorithm for the Identification of Rare Functional Dependencies in Complex Technical Infrastructures

Antonello¹,

Baraldi²,

Zio³

et al. 2020

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conf

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A Multi-Objective Evolutionary Algorithm (MOEA) is proposed for the identification of association rules describing functional dependencies in Complex Technical Infrastructures (CTIs). The algorithm uses novelty search to explore the solution space. It has been applied to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show its effectiveness in identifying rare functional dependencies not found using standard algorithms of Association Rule Mining (ARM) algorithms.

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Section: Letmentioning

confidence: 99%

Multi-Objective Evolutionary Algorithm for the Identification of Rare Functional Dependencies in Complex Technical Infrastructures

Antonello¹,

Baraldi²,

Zio³

et al. 2020

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conf

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“…The basic principle is to use speci c association rules to nd all constants whose support level is equal to or greater than the minimum value between support levels. Through future technical research, it will reach a more ideal height, exibly use various assembly languages, and improve application e ciency [5].…”

Section: Introductionmentioning

confidence: 99%

Application of medical intelligence based on Apriori algorithm in the management of rehabilitation nursing personnel

Shi

Wang²,

Guo

2023

Soft Comput

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With the increasing demand for medical services, the amount of data in the eld of health care is gradually increasing in a stepwise manner every year. These medical data actually capture the medical information of patients and the entire diagnosis and treatment process of doctors, and can re ect the actual situation in the medical and health eld. Currently, most hospital rehabilitation nurses use simple statistical methods to process medical data, and the use value of medical data resources is limited. To improve the value of using medical information, check the relationship between hidden laws and medical information. As one of the effective methods in the eld of data mining, Apriori's basic principle is to use speci c association rules to de ne all common element sets that support exceeding the minimum support threshold, and to create association rules that use a common model to achieve the validity threshold. This article uses Apriori algorithm to detect rehabilitation nursing personnel in hospitals, conduct data mining and exploration on medical data, design and construct a medical intelligence system, and then use Apriori algorithm to conduct research, propose a disease classi cation model and inpatient ow model, and conduct training and testing through the disease classi cation model. Finally, 300 hospital rehabilitation nursing personnel have received training in using a medical intelligence system to test the actual performance of the system, which has proven to enable rehabilitation nursing personnel to better understand patient information and provide personalized services.

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“…Furthermore, if the data are numeric, such as age, weight or length, these methods process the data by transforming numerical data into categorical data (i.e., a discretization process). This transformation process requires more time and can miss a significant amount of important information because data transformation does not maintain the main meaning of the original data [3], [4], [5]. For example, if age data represents a 35 years old person and is transformed to 1, this obscures the original meaning of the age information.…”

Section: Introductionmentioning

confidence: 99%

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Tahyudin¹,

Nambo²

2019

IJECE

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

show abstract

An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization

Cited by 32 publications

References 8 publications

Multi-Objective Evolutionary Algorithm for the Identification of Rare Functional Dependencies in Complex Technical Infrastructures

Multi-Objective Evolutionary Algorithm for the Identification of Rare Functional Dependencies in Complex Technical Infrastructures

Application of medical intelligence based on Apriori algorithm in the management of rehabilitation nursing personnel

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

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