A General Multiobjective Clustering Approach Based on Multiple Distance Measures

Liu, Cong; Liu, Jie; Peng, Dunlu; Wu, Chunxue

doi:10.1109/access.2018.2860791

Cited by 26 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, integrating multiple distance measures into clustering is studied. More recently, we proposed a novel approach 'multiobjective evolutionary clustering based on combining multiple distance measures' (MOECDM) to partition the dataset with different structures and an updated approach 'multiobjective evolutionary automatic clustering based on combining multiple distance measures' (MOEACDM) to detect the optimal cluster number [34]. Although both of the two approaches can get promising results, they have two limitations.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Liu

Chen

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Hence, in our paper, we employ Euclidean and Path distance measures as two distance measures. The two distance measures are also used in [34].…”

Section: Distance Measures Definition F-mfcmdm and F-mentioning

confidence: 99%

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Liu

Chen

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The work of compression is to remove redundant data, but it also needs to ensure the fidelity of compressed images and produce compressed images more and more real [38]. For the damaged roads in the disaster image, encoder E cannot effectively obtain the data of this part, and generator G needs to use the extracted noise v and compression representationŵ to preserve the road texture as much as possible, rather than simply synthesizing the data of this part.…”

Section: High Fidelity Image Compression Of Uav Based On Based Omentioning

confidence: 99%

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy. INDEX TERMS Complex disaster, convolutional neural networks, discriminator, generative adversarial networks, generator, high fidelity, image compression.

show abstract

“…Fuzzification coefficient α > 1 is a value that controls the fuzziness of clustering. To cluster well, D 2 ij , as dissimilarity measurement between the ith data and jth center, has been studied from different perspectives [25]- [29].…”

Section: Related Workmentioning

confidence: 99%

Multiple Kernel Fuzzy Clustering With Unsupervised Random Forests Kernel and Matrix-Induced Regularization

Zhao¹,

Chen²,

Gan

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Although kernel fuzzy clustering can handle non-spherical clusters by mapping data to a more separable feature space, its performance is highly determined by the setting of kernels. So, the multiple kernel fuzzy clustering (MKFC) is proposed to obtain the flexibility in designing an optimal kernel from a large set of candidates. In MKFC, many predefined general kernels like Gaussian and polynomial ones are linearly aggregated and the weights of kernels are adjusted automatically. However, the performance of MKFC is greatly hampered by two noticeable problems. First, MKFC only uses predefined general kernels and pays less attention to the inherent structure of specific data. This leads to the trouble of selecting proper base kernels for different data. The second problem is the ignorance of correlations between kernels in MKFC. It results in redundant kernels being used to define the feature space. This paper solves the two problems simultaneously by introducing a new MKFC model. Based on unsupervised random forests (RFs), some datadependent kernels are generated and combined with others to build a more representative feature space. The correlations between kernels are also calculated and inserted into the objective function of fuzzy clustering as a matrix-induced regularization to encourage the diversity in kernels. We name the new model as MKFC with unsupervised RFs kernel and matrix-induced regularization. The optimization algorithm for the new model is derived, and the experiments on benchmark datasets demonstrate its superiority over other MKFC approaches.INDEX TERMS Fuzzy clustering, multiple kernel, unsupervised random forests, regularization. I. INTRODUCTION

show abstract

A General Multiobjective Clustering Approach Based on Multiple Distance Measures

Cited by 26 publications

References 31 publications

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

Multiple Kernel Fuzzy Clustering With Unsupervised Random Forests Kernel and Matrix-Induced Regularization

Contact Info

Product

Resources

About