Mining structured data

Giovanni Martino, Alessandro Sperduti

Research output: Contribution to journalArticle

31 Citations (Scopus)

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 languageEnglish
Article number5386100
Pages (from-to)42-49
Number of pages8
JournalIEEE Computational Intelligence Magazine
Volume5
Issue number1
DOIs
Publication statusPublished - Feb 2010
Externally publishedYes

Fingerprint

Artificial intelligence
Mining
Neural networks
Processing
Graph Mining
Computational Intelligence
Kernel Methods
Graph in graph theory
Attribute
Neural Networks
Relationships
Class
Review
Human

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 journalArticle

Martino, Giovanni ; Sperduti, Alessandro. / Mining structured data. In: IEEE Computational Intelligence Magazine. 2010 ; Vol. 5, No. 1. pp. 42-49.
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