In recent years, the field of Machine Learning is showing great interest towards the processing of structured data, such as sequences, trees and graphs. In this paper an unsupervised recursive learning schema for structured data clustering is introduced. The schema allows to process data organized in graphs for both graph-focused and node-focused applications. The approach uses the Fuzzy C-Means algorithm as building block. Some experiments are proposed to show its performances and to compare it with another approach known in literature.

Fuzzy clustering of structured data: Some preliminary results

Ciaramella, Angelo
2017-01-01

Abstract

In recent years, the field of Machine Learning is showing great interest towards the processing of structured data, such as sequences, trees and graphs. In this paper an unsupervised recursive learning schema for structured data clustering is introduced. The schema allows to process data organized in graphs for both graph-focused and node-focused applications. The approach uses the Fuzzy C-Means algorithm as building block. Some experiments are proposed to show its performances and to compare it with another approach known in literature.
2017
9781509060344
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/63781
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact