Abstract
In many application domains, the amount of available data increased so much that humans need help from automatic computerized methods for extracting relevant information. Moreover, it is becoming more and more common to store data that possess inherently structural or relational characteristics. These types of data are best represented by graphs, which can very naturally represent entities, their attributes, and their relationships to other entities. In this article, we review the state of the art in graph mining, and we present advances in processing trees and graphs by two Computational Intelligence classes of methods, namely Neural Networks and Kernel Methods.
| Original language | English |
|---|---|
| Article number | 5386100 |
| Pages (from-to) | 42-49 |
| Number of pages | 8 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2010 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Artificial Intelligence
- Theoretical Computer Science
Cite this
Mining structured data. / Martino, Giovanni; Sperduti, Alessandro.
In: IEEE Computational Intelligence Magazine, Vol. 5, No. 1, 5386100, 02.2010, p. 42-49.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Mining structured data
AU - Martino, Giovanni
AU - Sperduti, Alessandro
PY - 2010/2
Y1 - 2010/2
N2 - In many application domains, the amount of available data increased so much that humans need help from automatic computerized methods for extracting relevant information. Moreover, it is becoming more and more common to store data that possess inherently structural or relational characteristics. These types of data are best represented by graphs, which can very naturally represent entities, their attributes, and their relationships to other entities. In this article, we review the state of the art in graph mining, and we present advances in processing trees and graphs by two Computational Intelligence classes of methods, namely Neural Networks and Kernel Methods.
AB - In many application domains, the amount of available data increased so much that humans need help from automatic computerized methods for extracting relevant information. Moreover, it is becoming more and more common to store data that possess inherently structural or relational characteristics. These types of data are best represented by graphs, which can very naturally represent entities, their attributes, and their relationships to other entities. In this article, we review the state of the art in graph mining, and we present advances in processing trees and graphs by two Computational Intelligence classes of methods, namely Neural Networks and Kernel Methods.
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UR - http://www.scopus.com/inward/citedby.url?scp=76349109072&partnerID=8YFLogxK
U2 - 10.1109/MCI.2009.935308
DO - 10.1109/MCI.2009.935308
M3 - Article
AN - SCOPUS:76349109072
VL - 5
SP - 42
EP - 49
JO - IEEE Computational Intelligence Magazine
JF - IEEE Computational Intelligence Magazine
SN - 1556-603X
IS - 1
M1 - 5386100
ER -