Hybrid Symbiotic Organism Search algorithms for Automatic Data Clustering

Rajah, V.; Ezugwu, Absalom E.

doi:10.1109/ictas47918.2020.234001

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…Rajah and Ezugwu [ 1 ] proposed and implemented four SOS-based hybrid algorithms–SOSFA, SOSDE, SOSTLBO, and SOSPSO for automatic partitioning of datasets without prior knowledge of the number of clusters in the datasets. Their main goal was to improve the overall performance of the basic SOS algorithm using a hybridization approach for automatic clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The simulation results of SOSK-means and results of existing algorithms published in the literature were compared to evaluate their performance. The algorithms include SOSTLBO [ 1 ], SOSFA [ 1 ], SOSPSO [ 1 ], SOSDE [ 1 ], DE [ 91 ], DCPSO [ 92 ]and GCUK [ 93 ]. Tables one contains the parameter settings for the SOSK-means, while Table 2 contains the setting for the other algorithms from the literature.…”

Section: Experimentationmentioning

confidence: 99%

“…Cluster analysis is an aspect of data analysis where data objects are distinctly grouped into clusters. The data objects within the same cluster share more intrinsic characteristics and differ from data objects in other clusters [ 1 ]. The primary objective of data clustering is to minimize the intra-cluster distances while maximizing the inter-cluster distances.…”

Section: Introductionmentioning

confidence: 99%

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Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Ikotun

Ezugwu

2022

PLoS ONE

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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Ikotun

Ezugwu

2022

PLoS ONE

Self Cite

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“…Comparisons with other hybrid algorithms from the literature (ACDE, DCPSO, and GCUK) were also performed, and FAPSO outperformed the competing hybrid algorithms. Rajah and Ezugwu [37] reported a hybrid of SOS with FA (SOSFA) alongside other SOS-based hybrid algorithms for handling automatic clustering. SOSFA was reported as having superior performance in some of the datasets used during the algorithms' performance investigation.…”

Section: Related Researchmentioning

confidence: 99%

“…Using a firefly-based K-means hybrid algorithm, a meteorological system was built to accurately and quickly estimate reference evapotranspiration [37]. Reference evapotranspiration is used in designing irrigation schedules, determining crop water requirements, and planning and managing agricultural water resources when the available meteorological data are limited.…”

Section: Related Researchmentioning

confidence: 99%

Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets

Ikotun

Ezugwu

2022

Applied Sciences

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Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants.

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Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

Ezugwu

Shukla

Agbaje

et al. 2020

Neural Comput & Applic

109

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Hybrid Symbiotic Organism Search algorithms for Automatic Data Clustering

Cited by 10 publications

References 24 publications

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets

Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

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