Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering Article Swipe
Dan Xue
,
Sen-Yuan Pang
,
Ning Liu
,
Shangkun Liu
,
Weimin Zheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13214215
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13214215
The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13214215
- OA Status
- gold
- Cited By
- 4
- References
- 82
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4403836749Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13214215Digital Object Identifier
- Title
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Phase-Angle-Encoded Snake Optimization Algorithm for K-Means ClusteringWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-27Full publication date if available
- Authors
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Dan Xue, Sen-Yuan Pang, Ning Liu, Shangkun Liu, Weimin ZhengList of authors in order
- Landing page
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https://doi.org/10.3390/electronics13214215Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/electronics13214215Direct OA link when available
- Concepts
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Cluster analysis, Algorithm, Computer science, Optimization algorithm, Mathematical optimization, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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82Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CMC | 123 |
| abstract_inverted_index.The | 0, 22, 50, 64, 95, 159 |
| abstract_inverted_index.all | 155 |
| abstract_inverted_index.and | 60, 73, 105, 122, 130, 173 |
| abstract_inverted_index.are | 116 |
| abstract_inverted_index.but | 27 |
| abstract_inverted_index.for | 19, 47, 70 |
| abstract_inverted_index.has | 28 |
| abstract_inverted_index.its | 112 |
| abstract_inverted_index.low | 29, 174 |
| abstract_inverted_index.sum | 131 |
| abstract_inverted_index.the | 35, 78, 126, 167 |
| abstract_inverted_index.Data | 11 |
| abstract_inverted_index.data | 48, 170 |
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| abstract_inverted_index.mark | 153 |
| abstract_inverted_index.over | 143 |
| abstract_inverted_index.poor | 61 |
| abstract_inverted_index.rate | 129 |
| abstract_inverted_index.show | 137 |
| abstract_inverted_index.slow | 57 |
| abstract_inverted_index.test | 147 |
| abstract_inverted_index.this | 33, 163 |
| abstract_inverted_index.uses | 67 |
| abstract_inverted_index.with | 107 |
| abstract_inverted_index.Iris, | 119 |
| abstract_inverted_index.Wine, | 120 |
| abstract_inverted_index.angle | 80 |
| abstract_inverted_index.based | 41 |
| abstract_inverted_index.error | 128 |
| abstract_inverted_index.faces | 17 |
| abstract_inverted_index.other | 108, 140 |
| abstract_inverted_index.paper | 164 |
| abstract_inverted_index.phase | 68, 79 |
| abstract_inverted_index.rapid | 1 |
| abstract_inverted_index.snake | 37, 54 |
| abstract_inverted_index.speed | 59 |
| abstract_inverted_index.their | 7 |
| abstract_inverted_index.using | 125 |
| abstract_inverted_index.while | 85 |
| abstract_inverted_index.θ-SO | 66, 99, 138 |
| abstract_inverted_index.Seeds, | 121 |
| abstract_inverted_index.across | 154 |
| abstract_inverted_index.angles | 69 |
| abstract_inverted_index.issues | 168 |
| abstract_inverted_index.method | 160 |
| abstract_inverted_index.paper, | 34 |
| abstract_inverted_index.proves | 6 |
| abstract_inverted_index.tasks. | 158 |
| abstract_inverted_index.tested | 117 |
| abstract_inverted_index.vector | 81 |
| abstract_inverted_index.CEC2013 | 103, 146 |
| abstract_inverted_index.K-means | 23 |
| abstract_inverted_index.enables | 74 |
| abstract_inverted_index.enhance | 92 |
| abstract_inverted_index.hitting | 149 |
| abstract_inverted_index.include | 56 |
| abstract_inverted_index.mapping | 43 |
| abstract_inverted_index.results | 136 |
| abstract_inverted_index.setting | 72 |
| abstract_inverted_index.(θ-SO), | 40 |
| abstract_inverted_index.Gaussian | 88 |
| abstract_inverted_index.boundary | 71 |
| abstract_inverted_index.compared | 106 |
| abstract_inverted_index.datasets | 104 |
| abstract_inverted_index.improved | 65 |
| abstract_inverted_index.problem, | 16 |
| abstract_inverted_index.proposed | 46, 161 |
| abstract_inverted_index.strategy | 90 |
| abstract_inverted_index.accuracy. | 21, 31, 63, 94, 176 |
| abstract_inverted_index.addresses | 166 |
| abstract_inverted_index.algorithm | 24, 39 |
| abstract_inverted_index.datasets, | 124 |
| abstract_inverted_index.efficient | 75 |
| abstract_inverted_index.employing | 86 |
| abstract_inverted_index.evaluated | 101 |
| abstract_inverted_index.strategy, | 44 |
| abstract_inverted_index.surpasses | 139 |
| abstract_inverted_index.accelerate | 83 |
| abstract_inverted_index.advantages | 8 |
| abstract_inverted_index.algorithms | 5, 141 |
| abstract_inverted_index.challenges | 18 |
| abstract_inverted_index.clustering | 30, 113, 156, 171, 175 |
| abstract_inverted_index.difficulty | 172 |
| abstract_inverted_index.distances. | 134 |
| abstract_inverted_index.functions, | 148 |
| abstract_inverted_index.adjustments | 76 |
| abstract_inverted_index.algorithms. | 110 |
| abstract_inverted_index.clustering, | 12 |
| abstract_inverted_index.clustering. | 49 |
| abstract_inverted_index.convergence | 58 |
| abstract_inverted_index.development | 2 |
| abstract_inverted_index.effectively | 165 |
| abstract_inverted_index.performance | 97 |
| abstract_inverted_index.traditional | 53 |
| abstract_inverted_index.Experimental | 135 |
| abstract_inverted_index.capabilities | 115 |
| abstract_inverted_index.convergence, | 84 |
| abstract_inverted_index.distribution | 89 |
| abstract_inverted_index.optimization | 15, 38, 55, 62, 93, 96, 114, 157 |
| abstract_inverted_index.Additionally, | 111 |
| abstract_inverted_index.disadvantages | 51 |
| abstract_inverted_index.intra-cluster | 133 |
| abstract_inverted_index.metaheuristic | 4, 109 |
| abstract_inverted_index.optimization. | 10 |
| abstract_inverted_index.traditaaional | 26 |
| abstract_inverted_index.classification | 127 |
| abstract_inverted_index.high-performance | 152 |
| abstract_inverted_index.phase-angle-encoded | 36 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5108050911 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I200845125 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.88299916 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |