A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Yeoh, Jia Ming; Caraffini, Fabio; Homapour, Elmina; Santucci, Valentino; Milani, Alfredo

doi:10.3390/math7121229

Cited by 24 publications

(12 citation statements)

References 58 publications

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“…Algorithms ABC, ACO, and AFSA are the second-level performers. Additionally, SI algorithms such as the whale optimization algorithm (WOA) and BA, and metaheuristic algorithm differential evolution (DE), were combined with the proposed method OpStream for data stream clustering and these results were compared with the state-of-art algorithms DenStream and CluStream [ 19 , 20 , 21 ]. Lu et al [ 22 ] proposed the text clustering PSO (TCPSO) algorithm by extending the PSO algorithm to solve the variable weighing in text clustering and compared these results with a few other algorithms such as bisection K-means and K-means.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Selvaraj

Choi

2021

Sensors

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Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

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Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Selvaraj

Choi

2021

Sensors

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“…This limits the choices of suitable optimisation methods to meta-heuristic algorithms. Amongst them, we selected 5 state-of-the-art algorithms belonging to different stochastic optimisation paradigms, including evolutionary computing, swarm intelligence and Bayesian optimisation [27][28][29][30][31]. Given the black-box nature of the problem at hand, these heterogeneous set of algorithms has been purposely chosen to compensate for the absence of information on the so-called 'fitness landscape' of the objective functions-also referred to as fitness function in the field-which prevents us from knowing a-priori which heuristic will perform best.…”

Section: Formulating the Optimisation Problemmentioning

confidence: 99%

Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

et al. 2021

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Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.

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“…Metaheuristics also apply to unsupervised learning techniques, such as clustering techniques. For example, in [50] a metaheuristic optimization was used for a clustering system for dynamic data streams. Metaheuristics have also been integrated into clustering techniques in the search for the centroids that best group the data under a certain metric.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

2020

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The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Cited by 24 publications

References 58 publications

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

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