Consensus clustering using kNN mode seeking. Myhre, J., N.; Mikalsen, K., Ø.; Løkse, S.; and Jenssen, R. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015.
abstract   bibtex   
© Springer International Publishing Switzerland 2015. In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In combining these frameworks, two well known problematic issues are directly bypassed; the kernel bandwidth choice of the kernel density based mean shift and the computational complexity of the mean shift iterations. We demonstrate experiments on both real and synthetic data as a proof of concept for our contributions.
@inproceedings{
 title = {Consensus clustering using kNN mode seeking},
 type = {inproceedings},
 year = {2015},
 identifiers = {[object Object]},
 id = {b8b495e1-1bdc-34d0-aa6a-2ad6998aa378},
 created = {2018-11-19T15:53:38.399Z},
 file_attached = {false},
 profile_id = {00330907-7fcb-39a3-a388-db79231d75d8},
 group_id = {a4d8c5ee-7397-327b-a54b-330e0b4d7061},
 last_modified = {2018-11-19T15:53:38.493Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© Springer International Publishing Switzerland 2015. In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In combining these frameworks, two well known problematic issues are directly bypassed; the kernel bandwidth choice of the kernel density based mean shift and the computational complexity of the mean shift iterations. We demonstrate experiments on both real and synthetic data as a proof of concept for our contributions.},
 bibtype = {inproceedings},
 author = {Myhre, Jonas Nordhaug and Mikalsen, Karl Øyvind and Løkse, Sigurd and Jenssen, Robert},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}
Downloads: 0