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Hierarchical Data Organization, Clustering and Denoising via Localized Diffusion Folders
 

Summary: Hierarchical Data Organization, Clustering and Denoising via
Localized Diffusion Folders
Gil Davida1
and Amir Averbuchb
a
Department of Mathematics, Program in Applied Mathematics
Yale University, New Haven, CT 06510, USA
b
School of Computer Science, Tel-Aviv University
Tel-Aviv, 69978, Israel
Abstract
Data clustering is a common technique for data analysis. It is used in many fields including
machine learning, data mining, customer segmentation, trend analysis, pattern recognition
and image analysis. The proposed Localized Diffusion Folders (LDF) methodology, whose
localized folders are called diffusion folders (DF), introduces consistency criteria for hier-
archical folder organization, clustering and classification of high-dimensional datasets. The
DF are multi-level data partitioning into local neighborhoods that are generated by several
random selections of data points and DF in a diffusion graph and by redefining local dif-
fusion distances between them. This multi-level partitioning defines an improved localized
geometry for the data and a localized Markov transition matrix that is used for the next

  

Source: Averbuch, Amir - School of Computer Science, Tel Aviv University

 

Collections: Computer Technologies and Information Sciences