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The GH-EXIN neural network for hierarchical clustering
Giansalvo Cirrincione1, Gabriele Ciravegna2, Pietro Barbiero3
1University of South Pacific, Suva, Fiji; University of Picardie Jules Verne, Amiens, France.
Summary
The GH-EXIN neural network offers a data-driven, self-organized approach to hierarchical clustering, overcoming limitations of traditional methods. This method effectively builds hierarchical trees and analyzes complex datasets like gene expression for cancer research.
Area of Science:
- Computational Intelligence
- Machine Learning
- Bioinformatics
Background:
- Hierarchical clustering is crucial for multi-resolution data analysis.
- Divisive algorithms offer data-driven splitting but struggle with threshold settings.
- Neural networks can overcome threshold limitations due to their data-dependent nature.
Purpose of the Study:
- To introduce the growing hierarchical GH-EXIN neural network for data-driven hierarchical clustering.
- To address limitations in existing hierarchical clustering methods, particularly threshold setting.
- To demonstrate the network's utility in complex data analysis, including gene expression and image recognition.
Main Methods:
- Developed the GH-EXIN neural network, a top-down, incremental, and self-organized architecture.
- Employed an anisotropic region of influence based on neighborhood convex hull for horizontal growth.
- Implemented novel simultaneous reallocation and outlier detection across all network leaves.
Main Results:
- GH-EXIN successfully builds hierarchical trees in a data-driven manner.
- The network demonstrates advantages over existing methods, validated on synthetic and real-world data (image recognition).
- Effective application shown in two-way hierarchical clustering for colorectal cancer gene expression analysis.
Conclusions:
- GH-EXIN provides a robust, self-organized solution for hierarchical clustering.
- The novel neighborhood convex hull and simultaneous leaf processing enhance tree construction and data analysis.
- GH-EXIN shows significant promise for applications in bioinformatics and pattern recognition.
