Name
Title | ||
|---|---|---|
Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD |
Abstract | ||
|---|---|---|
In this paper we describe a way to discover attribute associations and a way to present them to users using Bayesian networks.
We describe a three-dimensional visualization to present them effectively to users. Furthermore we discuss two applications
of attribute associations to the KDD process. One application involves using them to support feature selection. The result
of our experiment shows that feature selection using visualized attribute associations works well in 17 data sets out of the
24 that were used. The other application uses them to support the selection of data mining methods. We discuss the possibility
of using attribute associations to help in deciding if a given data set is suited to learning decision trees. We found 3 types
of structural characteristics in Bayesian networks obtained from the data. The characteristics have strong relevance to the
results of learning decision trees.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/978-3-540-48247-5_7 | PKDD |
Keywords | Field | DocType |
bayesian networks,visualizing attribute,decision tree,data mining,feature selection,bayesian network,three dimensional | Information system,Data mining,Decision tree,Data set,Information processing,Feature selection,Visualization,Computer science,Bayesian network,Artificial intelligence,Machine learning,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1704 | 0302-9743 | 3-540-66490-4 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gou Masuda | 1 | 11 | 2.51 |
| Rei Yano | 2 | 0 | 0.34 |
| Norihiro Sakamoto | 3 | 43 | 10.32 |
| Kazuo Ushijima | 4 | 201 | 27.49 |