Fuzzy clustering of structured data: Some preliminary results
| Autor: | Angelo Ciaramella, Giuseppe Vettigli |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Clustering
Fuzzy Approaches Structured Data Software Theoretical Computer Science Artificial Intelligence Applied Mathematics Fuzzy clustering Computer science Correlation clustering 02 engineering and technology Machine learning computer.software_genre Fuzzy logic 0202 electrical engineering electronic engineering information engineering Cluster analysis Block (data storage) business.industry Graph Schema (genetic algorithms) Data stream clustering Canopy clustering algorithm 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | FUZZ-IEEE |
| DOI: | 10.1109/fuzz-ieee.2017.8015648 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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