Combining multiple weak clusterings

By Alexander P. Topchy, Anil K. Jain, William F. Punch III. Third IEEE International Conference on Data Mining, 2003

387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/56325bed4bb43018ab825d6f3e803535401997f8

How researchers see this paper

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

In Artificial Intelligence

1. Ludmila I. Kuncheva
2. Dmitry P. Vetrov
3. Alexander P. Topchy
4. William F. Punch III
5. Anil K. Jain
6. Xiao-Lei Zhang
7. Lior Rokach
8. Ying-jie Tian

In Computer Vision and Pattern Recognition

1. Ludmila I. Kuncheva
2. Dmitry P. Vetrov
3. William F. Punch III
4. Alexander P. Topchy
5. Anil K. Jain
6. Xiaogang Wang
7. Luc Van Gool
8. Arun A. Ross

Citations in the corpus

• Cluster ensembles (2011, neutral): “In Ref 25, the authors, however, viewed cluster ensemble as a median clustering problem and used generalized entropy to modify the NMI measure explained earlier.”
• A Mixture Model for Clustering Ensembles (2004, methodology): “The work [31] also employed ensembles of so-called weak clustering algorithms and demonstrated how accurate consensus can be obtained from not very reliable components.”
• Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “Among these are random initializations of the clustering algorithm, subsampling, or resampling the data [5], [8], [9], [13], [16], [26], [27], applying different types of clustering algorithms [1], [16], [18], [37], using subsets of features [16], [32], “weakening” the clustering algorithm [16], [34”