# Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond

arXiv: https://arxiv.org/abs/1710.03113 · HTML: https://mlsanity.com/paper/1710.03113/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 18 of 18)

- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Strongly Augmented Contrastive Clustering](https://mlsanity.com/paper/2206.00380/) (2022)
- [Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering](https://mlsanity.com/paper/2203.08060/) (2022)
- [Joint Multi-view Unsupervised Feature Selection and Graph Learning](https://mlsanity.com/paper/2204.08247/) (2022)
- [Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning](https://mlsanity.com/paper/2209.04187/) (2022)
- [Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks](https://mlsanity.com/paper/2207.07173/) (2022)
- [Deep Temporal Contrastive Clustering](https://mlsanity.com/paper/2212.14366/) (2022)
- [Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity](https://mlsanity.com/paper/2405.06904/) (2024)
- [DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks](https://mlsanity.com/paper/2206.00359/) (2022)
- [Heterogeneous Tri-stream Clustering Network](https://mlsanity.com/paper/2301.04451/) (2023)
- [Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering](https://mlsanity.com/paper/2411.00904/) (2024)
- [Vision Transformer for Contrastive Clustering](https://mlsanity.com/paper/2206.12925/) (2022)
- [fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering](https://mlsanity.com/paper/2107.02071/) (2021)
- [One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering](https://mlsanity.com/paper/2305.07386/) (2023)
- [Learning Representations for Clustering via Partial Information Discrimination and Cross-Level Interaction](https://mlsanity.com/paper/2401.13503/) (2024)
- [Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype Aggregation](https://mlsanity.com/paper/2401.13581/) (2024)
- [Dual-Level Cross-Modal Contrastive Clustering](https://mlsanity.com/paper/2409.04561/) (2024)
- [Improve Contrastive Clustering Performance by Multiple Fusing-Augmenting ViT Blocks](https://mlsanity.com/paper/2511.08883/) (2025)

## Abstract (arXiv)

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state-of-the-art. The source code is available at https://github.com/huangdonghere/MDEC.