Scalable Multi-Relational Association Mining

Amanda Clare; Hugh E. Williams; Nicholas Lester

doi:10.1109/ICDM.2004.10035

Scalable Multi-Relational Association Mining

Amanda Clare, Hugh E. Williams, Nicholas Lester

Department of Computer Science

Research output: Contribution to conference › Paper

7 Citations (Scopus)

Abstract

We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools.

Original language	English
Pages	355-358
Number of pages	4
DOIs	https://doi.org/10.1109/ICDM.2004.10035
Publication status	Published - 2004

Access to Document

10.1109/ICDM.2004.10035

http://hdl.handle.net/2160/127

Permanent link

Persistent link

Cite this

@conference{0b407b95e69c407f9675a60d9c76cdfc,

title = "Scalable Multi-Relational Association Mining",

abstract = "We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools.",

author = "Amanda Clare and Williams, {Hugh E.} and Nicholas Lester",

note = "Clare, A., Williams, H. E. and Lester, N. M. (2004) Scalable Multi-Relational Association Mining. In proceedings of the 4th International Conference on Data Mining ICDM '04.",

year = "2004",

doi = "10.1109/ICDM.2004.10035",

language = "English",

pages = "355--358",

}

TY - CONF

T1 - Scalable Multi-Relational Association Mining

AU - Clare, Amanda

AU - Williams, Hugh E.

AU - Lester, Nicholas

N1 - Clare, A., Williams, H. E. and Lester, N. M. (2004) Scalable Multi-Relational Association Mining. In proceedings of the 4th International Conference on Data Mining ICDM '04.

PY - 2004

Y1 - 2004

N2 - We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools.

AB - We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools.

UR - http://www.scopus.com/inward/record.url?scp=19544393715&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2004.10035

DO - 10.1109/ICDM.2004.10035

M3 - Paper

SP - 355

EP - 358

ER -

Scalable Multi-Relational Association Mining

Abstract

Access to Document

Permanent link

Other files and links

Fingerprint

Cite this