Clustering pair-wise dissimilarity data into partially ordered sets

Jinze Liu; Qi Zhang; Wei Wang; Leonard McMillan; Jan Prins

doi:10.1145/1150402.1150480

Clustering pair-wise dissimilarity data into partially ordered sets

Jinze Liu, Qi Zhang, Wei Wang, Leonard McMillan, Jan Prins

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Ontologies represent data relationships as hierarchies of possibly overlapping classes. Ontologies are closely related to clustering hierarchies, and in this article we explore this relationship in depth. In particular, we examine the space of ontologies that can be generated by pairwise dissimilarity matrices. We demonstrate that classical clustering algorithms, which take dissimilarity matrices as inputs, do not incorporate all available information. In fact, only special types of dissimilarity matrices can be exactly preserved by previous clustering methods. We model ontologies as a partially ordered set (poset) over the subset relation. In this paper, we propose a new clustering algorithm, that generates a partially ordered set of clusters from a dissimilarity matrix.

Original language	English
Title of host publication	KDD 2006
Subtitle of host publication	Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages	637-642
Number of pages	6
DOIs	https://doi.org/10.1145/1150402.1150480
State	Published - 2006
Event	KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States Duration: Aug 20 2006 → Aug 23 2006

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	2006

Conference

Conference	KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/Territory	United States
City	Philadelphia, PA
Period	8/20/06 → 8/23/06

Keywords

Clustering
Dissimilarity
PoCluster
Poset

ASJC Scopus subject areas

Information Systems

Access to Document

10.1145/1150402.1150480

Cite this

Liu, J., Zhang, Q., Wang, W., McMillan, L., & Prins, J. (2006). Clustering pair-wise dissimilarity data into partially ordered sets. In KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 637-642). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006). https://doi.org/10.1145/1150402.1150480

@inproceedings{308f76182201419a85c2f35b8c6046dd,

title = "Clustering pair-wise dissimilarity data into partially ordered sets",

abstract = "Ontologies represent data relationships as hierarchies of possibly overlapping classes. Ontologies are closely related to clustering hierarchies, and in this article we explore this relationship in depth. In particular, we examine the space of ontologies that can be generated by pairwise dissimilarity matrices. We demonstrate that classical clustering algorithms, which take dissimilarity matrices as inputs, do not incorporate all available information. In fact, only special types of dissimilarity matrices can be exactly preserved by previous clustering methods. We model ontologies as a partially ordered set (poset) over the subset relation. In this paper, we propose a new clustering algorithm, that generates a partially ordered set of clusters from a dissimilarity matrix.",

keywords = "Clustering, Dissimilarity, PoCluster, Poset",

author = "Jinze Liu and Qi Zhang and Wei Wang and Leonard McMillan and Jan Prins",

year = "2006",

doi = "10.1145/1150402.1150480",

language = "English",

isbn = "1595933395",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

pages = "637--642",

booktitle = "KDD 2006",

note = "KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 20-08-2006 Through 23-08-2006",

}

Liu, J, Zhang, Q, Wang, W, McMillan, L & Prins, J 2006, Clustering pair-wise dissimilarity data into partially ordered sets. in KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2006, pp. 637-642, KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, United States, 8/20/06. https://doi.org/10.1145/1150402.1150480

Clustering pair-wise dissimilarity data into partially ordered sets. / Liu, Jinze; Zhang, Qi; Wang, Wei et al.
KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006. p. 637-642 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clustering pair-wise dissimilarity data into partially ordered sets

AU - Liu, Jinze

AU - Zhang, Qi

AU - Wang, Wei

AU - McMillan, Leonard

AU - Prins, Jan

PY - 2006

Y1 - 2006

N2 - Ontologies represent data relationships as hierarchies of possibly overlapping classes. Ontologies are closely related to clustering hierarchies, and in this article we explore this relationship in depth. In particular, we examine the space of ontologies that can be generated by pairwise dissimilarity matrices. We demonstrate that classical clustering algorithms, which take dissimilarity matrices as inputs, do not incorporate all available information. In fact, only special types of dissimilarity matrices can be exactly preserved by previous clustering methods. We model ontologies as a partially ordered set (poset) over the subset relation. In this paper, we propose a new clustering algorithm, that generates a partially ordered set of clusters from a dissimilarity matrix.

AB - Ontologies represent data relationships as hierarchies of possibly overlapping classes. Ontologies are closely related to clustering hierarchies, and in this article we explore this relationship in depth. In particular, we examine the space of ontologies that can be generated by pairwise dissimilarity matrices. We demonstrate that classical clustering algorithms, which take dissimilarity matrices as inputs, do not incorporate all available information. In fact, only special types of dissimilarity matrices can be exactly preserved by previous clustering methods. We model ontologies as a partially ordered set (poset) over the subset relation. In this paper, we propose a new clustering algorithm, that generates a partially ordered set of clusters from a dissimilarity matrix.

KW - Clustering

KW - Dissimilarity

KW - PoCluster

KW - Poset

UR - http://www.scopus.com/inward/record.url?scp=33749556696&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749556696&partnerID=8YFLogxK

U2 - 10.1145/1150402.1150480

DO - 10.1145/1150402.1150480

M3 - Conference contribution

AN - SCOPUS:33749556696

SN - 1595933395

SN - 9781595933393

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 637

EP - 642

BT - KDD 2006

T2 - KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Y2 - 20 August 2006 through 23 August 2006

ER -

Liu J, Zhang Q, Wang W, McMillan L, Prins J. Clustering pair-wise dissimilarity data into partially ordered sets. In KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006. p. 637-642. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/1150402.1150480

Clustering pair-wise dissimilarity data into partially ordered sets

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this