An Improved Ant Colony Clustering Algorithm

Tao, Weian; Tian, Jinghua; Ma, Yuanliang; Duan, Wenshu; Liang, Yuanyuan

doi:10.1007/978-1-4471-2386-6_204

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…This idea is used by many researchers in their studies [9–12]. For example, Zhang and Cao [9] defined one evaluation function to evaluate the performance of the clustering algorithms, which is called “error rate (ER),” using this idea.…”

Section: Applicationsmentioning

confidence: 99%

“…In assessing the last three algorithms in Table 2, the average accuracy of the abstraction ant colony clustering algorithm is the best, but the difference between the highest accuracy and lowest accuracy, at 5.33%, is not the least; this distinction belongs to the improved ant colony clustering algorithm [12], at 1%. This means that the computational stability of the improved ant colony clustering algorithm is the best.…”

Section: Applicationsmentioning

confidence: 99%

“…Tan et al [22] proposed a simplified ant-based clustering (SABC) method based on existing research of a state-of-the-art ant-based clustering system. Tao et al [12] redefined the distance between two data objects and improved the strategy for ants letting go and picking up data objects, thus proposing an improved ant colony clustering algorithm. Wang et al [10] proposed an improvement to the ATTA called Logic-Based Cold Ants (LCA).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Ant Colony Clustering Algorithm and Its Performance Study

Gao

2016

Computational Intelligence and Neuroscience

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Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%