Lin SD, Chalupsky H. "Unsupervised link discovery in multi-relational data via rarity analysis" Third IEEE International Conference on Data Mining, Proceedings: 171-178, 2003

[Eugene Garfield]

E-mail Addresses:

Shou-de Lin : sdlin at isi.edu
Hans Chalupsky : hans at isi.edu


Title: Unsupervised link discovery in multi-relational data via rarity analysis

Author(s): Lin SD, Chalupsky H

Source: THIRD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS :
171-178, 2003

Editor(s): Wu XD, Tuzhilin A, Shavlik J

Document Type: Article
Language: English
Cited References: 20

Abstract:
A significant portion of knowledge discovery and data mining research
focuses on finding patterns of interest in data. Once a pattern is found, it
can be used to recognize satisfying instances. The new area of link
discovery requires a complementary approach, since patterns Of interest
might not yet be known or might have too few examples to be learnable. This
paper presents an unsupervised link discovery method aimed at discovering
unusual, interestingly linked entities in multi-relational datasets. Various
notions of rarity are introduced to measure the "interestingness" of sets of
paths and entities. These measurements have been implemented and applied to
a real-world bibliographic dataset where they give very promising results.

Addresses: Lin SD (reprint author), Univ So Calif, Dept Comp Sci, Los
Angeles, CA 90089 USA
Univ So Calif, Dept Comp Sci, Los Angeles, CA 90089 USA

Publisher: IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS
ALAMITOS, CA 90720-1264 USA

IDS Number: BY34Z
ISBN: 0-7695-1978-4


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