DocumentCode :
2369396
Title :
Unsupervised link discovery in multi-relational data via rarity analysis
Author :
Lin, Shou-de ; Chalupsky, Hans
Author_Institution :
Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA
fYear :
2003
fDate :
19-22 Nov. 2003
Firstpage :
171
Lastpage :
178
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. We present 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.
Keywords :
data analysis; data mining; distributed databases; pattern recognition; relational databases; unsupervised learning; data mining; data patterns; knowledge discovery; multirelational datasets; real-world bibliographic dataset; unsupervised link discovery method; Association rules; Computer science; Contracts; Data analysis; Data mining; Databases; Event detection; Pattern matching; Pattern recognition; Performance evaluation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2003. ICDM 2003. Third IEEE International Conference on
Print_ISBN :
0-7695-1978-4
Type :
conf
DOI :
10.1109/ICDM.2003.1250917
Filename :
1250917
Link To Document :
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