ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

Sơn, Lê Hoàng; Chiclana, Francisco; Kumar, Raghavendra; Mittal, Mamta; Khari, Manju; Chatterjee, Jyotir Moy; Baik, Sung Wook

doi:10.1016/j.knosys.2018.04.038

Cited by 91 publications

(38 citation statements)

References 37 publications

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“…Future studies will investigate a new method to accelerate the speed of HSSASCA as well as apply it for solving other constrained nonlinear optimization functions [62][63][64][65][66][67][68][69][70][71][72][73][74][75].…”

Section: Cantilever Beam Designmentioning

confidence: 99%

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

Singh¹,

Sơn²,

Chiclana³

et al. 2019

Engineering with Computers

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The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others.

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Section: Cantilever Beam Designmentioning

confidence: 99%

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

Singh¹,

Sơn²,

Chiclana³

et al. 2019

Engineering with Computers

Self Cite

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“…Therefore, retention of customers is a priority for almost all organizations as gaining new customers can be much more expensive than keeping the existing ones. There are many data analysis methods that have been proposed to find the valuable knowledge from the data including data mining approaches in [1–5] and machine learning approaches in [6–10]. In machine learning, geodemographic segmentation [11–14] is an interesting topic with many applications.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis

Long

Sơn

Khari

et al. 2019

Computational Intelligence and Neuroscience

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Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis.

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“…Association rule mining aims to find out the association rules that satisfy predefined minimum support and confidence from a given database [7]. The basic algorithms of association rules include Apriori, FP-growth and other algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Zhou

2019

IEEE Access

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With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being. Association rule mining is an important part of data mining technology. Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them. Traditional association rule algorithms need to set minimum support and minimum confidence in advance. However, these two values are largely influenced by human subjectivity. Many scholars use average and weight to set these two values, but the effect is still not very good. In order to solve this problem, this paper proposed an improved algorithm of association rules-PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP-growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.

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ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

Cited by 91 publications

References 37 publications

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

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