A clustering algorithm based on integration of K-Means and PSO

Atabay, Habibollah Agh; Sheikhzadeh, Mohammad Javad; Torshizi, Mehdi

doi:10.1109/csiec.2016.7482110

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…In this case, the population of GA is initialized by the k-means algorithm to reach the best cluster centers; thereafter, the GA operators are applied with a new mutation strategy that depends on the extreme points in the cluster groups. Atabay et al (2016) introduced a clustering algorithm that combines the PSO and k-means algorithms. This integration resolves the sensitivity of k-means to the initial choice of centroids.…”

Section: Clustering Methodsmentioning

confidence: 99%

“…This results in accelerated convergence and improved outcomes for the PSO algorithm. (Pacifico & Ludermir, 2021;Atabay et al, 2016;Ratanavilisagul, 2020) social spider optimization (SSO) (Thalamala et al, 2020) symbiotic organism search (SOS) (Zhou et al, 2019) Clustering Fuzzy parametrization.…”

Section: Clustering Methodsmentioning

confidence: 99%

“…In general, the items listed in the Opportunities column of Table 3, refer to points that are being worked on in the cited references, such as methods and techniques that are being developed to improve the algorithm performance. In this sense, it is worth highlighting the estimation of initial parameters Atabay et al (2016), El-Shorbagy et al (2019 and Wu (2019) and the strengthening of exploitation strategies to avoid getting stuck in local optima (Li et al, 2021b;Pacifico & Ludermir, 2021). Furthermore, the use of optimization algorithms to minimize the classification error in machine learning algorithms (Agustina et al, 2019;Nagra et al, 2020;Wu, 2019).…”

Section: Research Questions Answersmentioning

confidence: 99%

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Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Clustering Methodsmentioning

confidence: 99%

Section: Clustering Methodsmentioning

confidence: 99%

Section: Research Questions Answersmentioning

confidence: 99%

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Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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“…Swarm algorithms are metaheuristic techniques inspired by nature, such as the behavior of collective beings such as bees [ 27 ], ants [ 28 ] and birds [ 29 ]. Swarm techniques have been applied to VQ codebook design [ 30 , 31 , 32 ].…”

Section: Swarm Techniques Applied To Vqmentioning

confidence: 99%

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

Severo,

Ferreira,

Spencer

et al. 2024

Sensors

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Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde–Buzo–Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly influences VQ performance, as the convergence speed and codebook quality depend on the initial codebook. A widely used initialization alternative is random initialization, in which the initial set of codevectors is drawn randomly from the training set. Other initialization methods can lead to a better quality of the designed codebooks. The present work evaluates the impacts of initialization strategies on swarm intelligence algorithms for codebook design in terms of the quality of the designed codebooks, assessed by the quality of the reconstructed images, and in terms of the convergence speed, evaluated by the number of iterations. Initialization strategies consist of a combination of codebooks obtained by initialization algorithms from the literature with codebooks composed of vectors randomly selected from the training set. The possibility of combining different initialization techniques provides new perspectives in the search for the quality of the VQ codebooks. Nine initialization strategies are presented, which are compared with random initialization. Initialization strategies are evaluated on the following algorithms for codebook design based on swarm clustering: modified firefly algorithm—Linde–Buzo–Gray (M-FA-LBG), modified particle swarm optimization—Linde–Buzo–Gray (M-PSO-LBG), modified fish school search—Linde–Buzo–Gray (M-FSS-LBG) and their accelerated versions (M-FA-LBGa, M-PSO-LBGa and M-FSS-LBGa) which are obtained by replacing the LBG with the accelerated LBG algorithm. The simulation results point out to the benefits of the proposed initialization strategies. The results show gains up to 4.43 dB in terms of PSNR for image Clock with M-PSO-LBG codebooks of size 512 and codebook design time savings up to 67.05% for image Clock, with M-FF-LBGa codebooks with size N=512, by using initialization strategies in substitution to Random initialization.

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“…Combining the robustness of PSO with traditional clustering has been a topic of significant research interest [19]. By amalgamating the deep search capabilities of PSO with clustering algorithms, researchers aim to harness the strengths of both worlds, yielding enhanced stability, accuracy, and performance [17]- [20].…”

Section: Introductionmentioning

confidence: 99%

SA-PSO-GK++: A New Hybrid Clustering Approach for Analyzing Medical Data

Abdo,

Abdelkader,

Abdel-Hamid

2024

IEEE Access

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Data clustering is an unsupervised learning task that has been extensively studied, given its wide applicability in various domains. Traditional algorithms often struggle to achieve a balance between exploration and exploitation, leading to sub-optimal solutions. This paper presents a novel hybrid algorithm named SA-PSO-GK++ that synergistically combines Particle Swarm Optimization (PSO), K-means++, Simulated Annealing (SA), and Gaussian Estimation of Distribution to tackle this issue effectively. The proposed SA-PSO-GK++ aims to overcome the drawbacks of existing methods by leveraging the strengths of each individual algorithm. The K-means++ initialization reduces the risk of poor initial centroids, while PSO aids in efficient search space exploration. GED provides a statistical model of the particle space, enabling the algorithm to generate new potential solutions that are statistically guided by the current best solutions. Additionally, the incorporation of Simulated Annealing allows the algorithm to escape local minima, thereby enhancing its global search capability. We evaluate the effectiveness of SA-PSO-GK++ using benchmark datasets from the UCI Machine Learning Repository, including the Iris, Breast cancer, Heart datasets and contraceptive method choice datasets. The proposed method outperforms conventional and some of the state-of-the-art hybrid clustering algorithms in terms of sum of euclidean distance, normalized index, and error rates. These advantages make SA-PSO-GK++ a compelling option for a wide range of clustering applications. The results offer promising avenues for future research in optimizing and applying this innovative clustering technique in diverse domains.

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A clustering algorithm based on integration of K-Means and PSO

Cited by 15 publications

References 13 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design

SA-PSO-GK++: A New Hybrid Clustering Approach for Analyzing Medical Data

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