Name
Abstract | ||
|---|---|---|
A significant portion of knowledge discovery and datamining research focuses on finding patterns of interest indata. Once a pattern is found, it can be used to recognizesatisfying instances. The new area of link discoveryrequires a complementary approach, since patterns ofinterest might not yet be known or might have too fewexamples to be learnable. This paper presents anunsupervised link discovery method aimed at discoveringunusual, interestingly linked entities in multi-relationaldatasets. Various notions of rarity are introduced tomeasure the "interestingness" of sets of paths andentities. These measurements have been implemented andapplied to a real-world bibliographic dataset where theygive very promising results. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICDM.2003.1250917 | ICDM |
Keywords | Field | DocType |
datamining research,unsupervised link discovery,real-world bibliographic dataset,complementary approach,anunsupervised link discovery method,rarity analysis,knowledge discovery,interest indata,patterns ofinterest,multi-relational data,promising result,paths andentities,new area,data mining,pattern recognition,distributed databases,information retrieval,satisfiability,unsupervised learning,relational data,data analysis,relational databases | Data mining,Information retrieval,Relational database,Computer science,Unsupervised learning,Knowledge extraction,Distributed database,K-optimal pattern discovery | Conference |
ISBN | Citations | PageRank |
0-7695-1978-4 | 35 | 3.34 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shou-De Lin | 1 | 706 | 84.81 |
| Hans Chalupsky | 2 | 358 | 44.48 |