AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm
Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K-means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution...
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| Vydané v: | Information sciences Ročník 672; s. 120661 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.06.2024
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K-means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K-means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results.
•AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations.•A fitness function is designed based on the clustering principle of “birds of a feather flock together”, with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm.•An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. |
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| AbstractList | Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K-means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K-means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results.
•AHA is introduced to address the problems of the sensitivity to initial cluster centers and the proneness to local optima. Hummingbirds are treated as data points, which dynamically update their strategies and effectively find the optimal cluster centers during multiple iterations.•A fitness function is designed based on the clustering principle of “birds of a feather flock together”, with the aim of simplifying calculations, which enhances the specificity and practicality of K-means algorithm.•An AHA-based three-way K-means clustering algorithm (i.e., AHA-3WKM) is proposed. The clustering process is initialized with cluster centers optimized by AHA, and the results are represented in three regions, which can capture the uncertainty within the datasets. |
| ArticleNumber | 120661 |
| Author | Chen, Xiying Miao, Duoqian Lin, Bowen Liu, Caihui Lai, Jianying |
| Author_xml | – sequence: 1 givenname: Xiying orcidid: 0009-0007-7314-0492 surname: Chen fullname: Chen, Xiying email: xiying_chen@163.com organization: Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 34100, Jiangxi, China – sequence: 2 givenname: Caihui orcidid: 0000-0003-2636-0613 surname: Liu fullname: Liu, Caihui email: liucaihui@gnnu.edu.cn, liu_caihui@163.com organization: Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 34100, Jiangxi, China – sequence: 3 givenname: Bowen surname: Lin fullname: Lin, Bowen email: lin_bw@qq.com organization: Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 34100, Jiangxi, China – sequence: 4 givenname: Jianying orcidid: 0000-0003-4682-071X surname: Lai fullname: Lai, Jianying email: 1421631021@qq.com organization: Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 34100, Jiangxi, China – sequence: 5 givenname: Duoqian orcidid: 0000-0001-6588-1468 surname: Miao fullname: Miao, Duoqian email: dqmiao@tongji.edu.cn organization: Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China |
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| Keywords | Three-way clustering Artificial hummingbird algorithm Fitness function Cluster centers K-means clustering |
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