Paper Info
Title
Unsupervised Extremely Randomized Trees.
Abstract
In this paper we present a method to compute dissimilarities on unlabeled data, based on extremely randomized trees. This method, Unsupervised Extremely Randomized Trees, is used jointly with a novel randomized labeling scheme we describe here, and that we call AddCl3. Unlike existing methods such as AddCl1 and AddCl2, no synthetic instances are generated, thus avoiding an increase in the size of the dataset. The empirical study of this method shows that Unsupervised Extremely Randomized Trees with AddCl3 provides competitive results regarding the quality of resulting clusterings, while clearly outperforming previous similar methods in terms of running time.
Year
Venue
Field
2018
PAKDD
Data mining,Decision tree,Similarity measure,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Empirical research
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kevin Dalleau100.68
Miguel Couceiro222951.87
Malika Smaïl-Tabbone38613.49