Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining

Altay, Elif Varol; Alataş, Bilal

doi:10.1016/j.ins.2020.12.055

Cited by 40 publications

(18 citation statements)

References 23 publications

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“…The results have been compared with other methods, and the BOSCA is superior to the others in dealing with these real-world optimization problems. Altay and Alatas (2021) proposed a hybrid DE and SCA for directly and rapidly mining the reduced high-quality numerical association rules. The proposed algorithm ensures the discovered rules with high confidence.…”

Section: Hybrid Sca Algorithmsmentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

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Section: Hybrid Sca Algorithmsmentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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“…There are several MOEA-based methods presented for addressing the problem that can be solved by the SOEA-based method [29][30][31][32] . For example, an MOEAbased method in terms of the fuzzy emerging patterns mining was designed [30] , a hybrid MOEA method in terms of the rapidly and directly high-quality rules ming was presented [31] , and an MOEA with an indexed set representation scheme in terms of the high utility patterns mining was presented [32] . Furthermore, in recent years, many works were presented to solve the privacy-preserving association rules mining problem.…”

Section: Evolutionary Algorithm-based Miningmentioning

confidence: 99%

An Optimized Sanitization Approach for Minable Data Publication

Yang

Liao

2022

Big Data Min. Anal.

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Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle.Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.

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“…If special points are found in the processing process, a smoother and more stable curve can be obtained [18]. is algorithm will obtain uneven curves without obvious specificity [19]. Based on relevant knowledge, the literature divides sequential pattern mining into the following: (1) sorting.…”

Section: Related Workmentioning

confidence: 99%

Application of Data Mining System in User Network Environment Based on SVM Optimization Algorithm

Yan-ying

2022

Mobile Information Systems

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Nowadays, different types of data and information combine and interact with each other, forming a complex and huge information network. Using data mining technology, one can effectively obtain the hidden data contained in the data bureau. This technology is the most commonly used way to obtain network target data at present. In this paper, we try to practically apply related algorithms by studying the theory of multi-information fusion. Aiming at the diversity and practicality of the network, the multi-information fusion method was optimized and improved on the basis of the traditional multi-information fusion method. Secondly, a data mining system based on the concept and algorithm of association rules is established, which simplifies the working mode of frequent mining and then improves the data mining model. Finally, an empirical analysis is designed. A group of data samples are selected from the network for preliminary processing, and the data set is brought into the system for testing. From the test results, it can be seen that the algorithm designed in this paper can effectively obtain the target data and works well in a complex network environment, can analyze meaningful data association using user network rules, and provides important guidance for optimizing network information and improving extraction efficiency. This paper combines data mining technology and multi-information fusion technology to conduct in-depth research and further complete the algorithm design by combining the two technologies, which proves the accuracy and processing efficiency of the algorithm.

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Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining

Cited by 40 publications

References 23 publications

A comprehensive survey on the sine–cosine optimization algorithm

A comprehensive survey on the sine–cosine optimization algorithm

An Optimized Sanitization Approach for Minable Data Publication

Application of Data Mining System in User Network Environment Based on SVM Optimization Algorithm

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