The Experts below are selected from a list of 57996 Experts worldwide ranked by ideXlab platform
Chiehyuan Tsai - One of the best experts on this subject based on the ideXlab platform.
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an application of a metaheuristic algorithm based clustering ensemble method to app customer segmentation
Neurocomputing, 2016Co-Authors: R J Kuo, C H Mei, Ferani E Zulvia, Chiehyuan TsaiAbstract:This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co-Association Matrix in the co-occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic-based clustering algorithms are proposed. This paper uses a real-coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic-based clustering ensembles algorithms have better performance than metaheuristic-based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization-based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result.
Deyu Zeng - One of the best experts on this subject based on the ideXlab platform.
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scalable spectral ensemble clustering via building representative co Association Matrix
Neurocomputing, 2020Co-Authors: Yinian Liang, Zhigang Ren, Deyu ZengAbstract:Abstract Ensemble clustering via building co-Association Matrix and combining multiple basic partitions from the same dataset into the consensus one has been widely used in spectral clustering and subspace clustering. However, with the ever-increasing cost of calculating the co-Association Matrix, the conventional ensemble clustering algorithm is no longer fit for dealing with the large-scale datasets due to its less scalability and time-consuming. In this paper, we propose a scalable spectral ensemble clustering method via building a representative co-Association Matrix to improve the ensemble clustering problem. Our method mainly includes constructing a sparse Matrix to select the representative points and building the co-Association Matrix, and a robust and denoising representation for the co-Association Matrix can be learned through a low-rank constraint in a unified optimization framework. The experiments verify the high efficiency and scalability but less time cost of our method compared with state-of-art clustering methods in the six real-world datasets, especially in the large-scale datasets.
Zhigang Ren - One of the best experts on this subject based on the ideXlab platform.
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scalable spectral ensemble clustering via building representative co Association Matrix
Neurocomputing, 2020Co-Authors: Yinian Liang, Zhigang Ren, Deyu ZengAbstract:Abstract Ensemble clustering via building co-Association Matrix and combining multiple basic partitions from the same dataset into the consensus one has been widely used in spectral clustering and subspace clustering. However, with the ever-increasing cost of calculating the co-Association Matrix, the conventional ensemble clustering algorithm is no longer fit for dealing with the large-scale datasets due to its less scalability and time-consuming. In this paper, we propose a scalable spectral ensemble clustering method via building a representative co-Association Matrix to improve the ensemble clustering problem. Our method mainly includes constructing a sparse Matrix to select the representative points and building the co-Association Matrix, and a robust and denoising representation for the co-Association Matrix can be learned through a low-rank constraint in a unified optimization framework. The experiments verify the high efficiency and scalability but less time cost of our method compared with state-of-art clustering methods in the six real-world datasets, especially in the large-scale datasets.
Dacheng Tao - One of the best experts on this subject based on the ideXlab platform.
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spectral ensemble clustering via weighted k means theoretical and practical evidence
IEEE Transactions on Knowledge and Data Engineering, 2017Co-Authors: Hongfu Liu, Tongliang Liu, Dacheng TaoAbstract:As a promising way for heterogeneous data analytics, consensus clustering has attracted increasing attention in recent decades. Among various excellent solutions, the co-Association Matrix based methods form a landmark, which redefines consensus clustering as a graph partition problem. Nevertheless, the relatively high time and space complexities preclude it from wide real-life applications. We, therefore, propose Spectral Ensemble Clustering (SEC) to leverage the advantages of co-Association Matrix in information integration but run more efficiently. We disclose the theoretical equivalence between SEC and weighted K-means clustering, which dramatically reduces the algorithmic complexity. We also derive the latent consensus function of SEC, which to our best knowledge is the first to bridge co-Association Matrix based methods to the methods with explicit global objective functions. Further, we prove in theory that SEC holds the robustness, generalizability, and convergence properties. We finally extend SEC to meet the challenge arising from incomplete basic partitions, based on which a row-segmentation scheme for big data clustering is proposed. Experiments on various real-world data sets in both ensemble and multi-view clustering scenarios demonstrate the superiority of SEC to some state-of-the-art methods. In particular, SEC seems to be a promising candidate for big data clustering.
R J Kuo - One of the best experts on this subject based on the ideXlab platform.
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an application of a metaheuristic algorithm based clustering ensemble method to app customer segmentation
Neurocomputing, 2016Co-Authors: R J Kuo, C H Mei, Ferani E Zulvia, Chiehyuan TsaiAbstract:This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co-Association Matrix in the co-occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic-based clustering algorithms are proposed. This paper uses a real-coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic-based clustering ensembles algorithms have better performance than metaheuristic-based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization-based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result.
