A Proposal of Interactive Growing Hierarchical SOM

TL;DR

This paper introduces an interactive Growing Hierarchical SOM that adaptively grows and prunes hierarchical maps, with a developed tool demonstrated on Iris data to improve data representation.

Contribution

It proposes a novel control method for GHSOM growth via pruning and develops an interactive tool to enhance usability and analysis.

Findings

Effective pruning reduces unnecessary hierarchy branches.

The interactive tool facilitates better understanding of data structure.

Successful application demonstrated on Iris dataset.

Abstract

Self Organizing Map is trained using unsupervised learning to produce a two-dimensional discretized representation of input space of the training cases. Growing Hierarchical SOM is an architecture which grows both in a hierarchical way representing the structure of data distribution and in a horizontal way representation the size of each individual maps. The control method of the growing degree of GHSOM by pruning off the redundant branch of hierarchy in SOM is proposed in this paper. Moreover, the interface tool for the proposed method called interactive GHSOM is developed. We discuss the computation results of Iris data by using the developed tool.