A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination
Joint Authors
Chen, Yingxia
Liu, Cong
Chen, Qianqian
Liu, Jie
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-17
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
Most of the existing clustering algorithms are often based on Euclidean distance measure.
However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures.
Thus, it is necessary to combine multiple distance measures into clustering.
However, the weights for different distance measures are hard to set.
Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique.
Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures.
However, it is time-consuming due to the large-sized genes.
This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures.
In this algorithm, a real encoding scheme is adopted to represent the individual.
Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual.
An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population.
In the final generation, a set of nondominated solutions can be obtained.
The best solution and the best distance measure are selected by using a semisupervised method.
Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically.
The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments.
Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.
American Psychological Association (APA)
Liu, Cong& Chen, Qianqian& Chen, Yingxia& Liu, Jie. 2019. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
Modern Language Association (MLA)
Liu, Cong…[et al.]. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering No. 2019 (2019), pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
American Medical Association (AMA)
Liu, Cong& Chen, Qianqian& Chen, Yingxia& Liu, Jie. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195298