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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Self-organizing subspace clustering for high-dimensional and multi-view data.

Aluizio F R Araújo¹, Victor O Antonino¹, Karina L Ponce-Guevara¹

¹Centro de Informática, Universidade Federal de Pernambuco, 50740560, Recife, Brazil.

Neural Networks : the Official Journal of the International Neural Network Society

|July 26, 2020

Summary

This study introduces a novel soft subspace clustering algorithm using a Self-organizing Map (SOM) with a dynamic structure. The method effectively clusters high-dimensional data without predefined parameters, outperforming existing approaches in various applications.

Keywords:

High-dimensional data Multi-view clustering Self-organizing maps Subspace clustering

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Area of Science:

Data Science
Machine Learning
Artificial Intelligence

Background:

Increasing data complexity and dimensionality pose challenges for traditional clustering methods.
Subspace clustering addresses the grouping of data points within a union of subspaces.
Existing subspace clustering algorithms often require prior knowledge of cluster numbers or network topology.

Purpose of the Study:

To introduce a novel soft subspace clustering algorithm.
To develop a Self-organizing Map (SOM) with a time-varying structure for adaptive clustering.
To enable clustering without prior knowledge of the number of categories or neural network topology.

Main Methods:

A soft subspace clustering algorithm based on a Self-organizing Map (SOM) with a time-varying structure.
The algorithm dynamically determines the number of categories and neural network topology during training.
Assigns relevancies (weights) to dimensions to capture their influence on cluster formation.

Main Results:

The algorithm demonstrates competitive performance across diverse high-dimensional datasets.
Effective in data mining, gene expression, multi-view, computer vision, and text clustering.
Consistently outperforms state-of-the-art subspace clustering methods in various contexts.

Conclusions:

The proposed soft subspace clustering algorithm offers a flexible and effective approach for high-dimensional data.
The dynamic nature of the SOM allows for adaptation to unknown data structures.
The method shows significant potential for advancing various data analysis and machine learning applications.