Solving cluster ensemble problems by bipartite graph partitioning

By Xiaoli Z. Fern, Carla E. Brodley. International Conference on Machine Learning, 2004

613 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b59a8bf45add9a7bae9e8c978862f8d3c1077b61

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. J. Lai
2. Dong Huang
3. Changdong Wang
4. Dacheng Tao
5. Zhenguo Li
6. Shih-Fu Chang
7. Luc Van Gool
8. Jiawei Han

In Electrical and Electronic Engineering

1. José Ruiz-Shulcloper
2. Sandro Vega-Pons
3. Jing Gao
4. Dong Huang
5. Jing Jiang
6. Jiawei Han
7. José Valente de Oliveira
8. Witold Pedrycz

In Statistical and Nonlinear Physics

1. Jiawei Han
2. José Ruiz-Shulcloper
3. Sandro Vega-Pons
4. Jing Gao
5. Dong Huang
6. Jing Jiang
7. Chee Keong Kwoh
8. José Valente de Oliveira

Citations in the corpus

• Time-series clustering - A decade review (2015, neutral): “in various studies [26,237,238].”
• Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019, methodology): “and Brodley [22] built a bipartite graph structure by treating both base clusters and data objects as graph nodes, and then partitioned the graph via the METIS algorithm [23].”
• Segmentation using superpixels: A bipartite graph partitioning approach (2012, background): “Also related is the work in [11] from machine learning, where bipartite graph partitioning is used for cluster ensemble.”
• Knowledge transfer via multiple model local structure mapping (2008, methodology): “Constructing a good ensemble of classifiers has been an active research area in supervised learning [12].”
• Locally Weighted Ensemble Clustering (2016, methodology): “N M In this section, we compare the proposed LWEA and LWGP methods against eleven ensemble clustering methods, namely, CSPA [17], HGPA [17], MCLA [17], hybrid bipartite graph formulation (HBGF) [19], EAC [20], weighted connected triple based method (WCT) [23], weighted evidence accumulation clusteri”
• Advances in Fuzzy Clustering and its Applications (2007, methodology): “This method was introduced by Fern and Brodley (2004) with an aim to model the instances and clusters simultaneously in a graph.”
• A CLUE for CLUster Ensembles (2005, neutral): “…in package e1071 ), the “evidence accumulation” framework of Fred and Jain (2002), the NMI optimization and graph-partitioning methods in Strehl and Ghosh (2003a), “Bagged Clustering” as in Dudoit and Fridlyand (2003), and the hybrid bipartite graph formulation of Fern and Brodley (2004).”
• A Survey on Multi-View Clustering (2017, neutral): “, [118], [119], [120], [121], [49] can be applied to MVC.”
• A Survey of Clustering Ensemble Algorithms (2011, methodology): “Another method, the Hybrid Bipartite Graph Formulation (HBGF) was introduced by Fern et al.(23) It models the clusters and the objects together in the same graph.”
• Cluster ensembles (2011, methodology): “Noting that CSPA and MCLA consider either the similarity of objects or similarity of clusters only, a hybrid bipartite graph formulation (HBGF) was proposed in Ref 45.”