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.File in questo prodotto:
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