WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory

Yousefnezhad, Muhammad; Huang, Sheng-Jun; Zhang, Daoqiang

doi:10.1109/tcyb.2016.2642999

Cited by 36 publications

(22 citation statements)

References 32 publications

(132 reference statements)

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“…(7) Hybrid clustering algorithms such as ensemble clustering [22][23][24] combine at least two kinds of the clustering algorithms mentioned above to get higher quality clustering results. Also, ensemble clustering algorithms using various strategies [25][26][27][28][29][30][31][32][33][34] to break through the limitations of base clustering algorithms have been increasingly popular in recent years. But these kinds of algorithms may have high time complexity.…”

Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.

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Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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“…Along this line, lots of efforts have been made, such as hierarchical consensus clustering [18] and spectral EC (SEC) [29]. In addition, some other representative EC methods include the framework using NMF [30], linked-based [31], bipartite graph [32], and wisdom-of-crowds [33]. Two recent works [34], [35] also learn robust representations from BPs to boost the EC performance, where [34] imposes a low-rank constraint on the coassociation matrix, and [35] feeds BPs into the stacked mDAs.…”

Section: A Related Workmentioning

confidence: 99%

Marginalized Multiview Ensemble Clustering

Tao

Liu

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Multiview clustering (MVC), which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single-view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M 2 VEC) method in this paper. Specifically, we solve MVC in an EC way, which generates BPs for each view individually and seeks for a consensus one. By this means, we naturally leverage the complementary information of multiview data upon the same partition space. In order to boost the robustness of our approach, the marginalized denoising process is adopted to mimic the data corruptions and noises, which provides robust partitionlevel representations for each view by training a single-layer autoencoder. A low-rank and sparse decomposition is seamlessly incorporated into the denoising process to explicitly capture the consistency information and meanwhile compensate the distinctness between heterogeneous features. Spectral consensus graph partitioning is also involved by our model to make M 2 VEC as a unified optimization framework. Moreover, a multilayer M 2 VEC is eventually delivered in a stacked fashion to encapsulate nonlinearity into partition-level representations for handling complex data. Experimental results on eight real-world data sets show the efficacy of our approach compared with several state-ofthe-art multiview and EC methods. We also showcase our method performs well with partial multiview data.

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“…We can see that there is a recursive relationship between τ c andh c , and the relations described in Eq. (12) and Eq. (13) determine bipartite spectral graph partition is based on diagonal co-cluster structure.…”

Section: E Clustering Ensemblementioning

confidence: 99%

Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

et al. 2020

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Clustering ensemble technique has been shown to be effective in improving the accuracy and stability of single clustering algorithms. With the development of information technology, the amount of data, such as image, text and video, has increased rapidly. Efficiently clustering these large-scale datasets is a challenge. Clustering ensembles usually transform clustering results to a co-association matrix, and then to a graph-partition problem. These methods may suffer from information loss when computing the similarity among samples or base clusterings. Rich information between samples and base clusterings is ignored. Moreover, the results are not discrete. They need post-processing steps to obtain the final clustering result, which will deviate greatly from the real clustering result. To address this problem, we propose a co-clustering ensemble based on bilateral k-means (CEBKM) algorithm. Our algorithm can simultaneously cluster samples and base clusterings of a dataset, to fully exploit the potential information between the samples and the base clusterings. In addition, it can directly obtain the final clustering results without using other clustering algorithms. The proposed method, outperformed several state-of-the-art clustering ensemble methods in experiments conducted on real-world and toy datasets. INDEX TERMS Clustering ensemble, bilateral k-means algorithm, co-clustering, base clustering.

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WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory

Cited by 36 publications

References 32 publications

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Marginalized Multiview Ensemble Clustering

Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

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