@inproceedings{53a33857604d4c5282ac6ae236539947,
title = "Distance metrics for time-series data with concentric multi-sphere self organizing maps",
abstract = "Self-Organizing Maps have been shown to be a powerful unsupervised learning a tool in the analysis of complex high dimensional data. SOMs are capable of performing topological mapping, clustering and dimensionality reduction in order to effectively visualize and understand data and it is desirable to apply these techniques to time-series data. In this project a novel approach to time-series learning using Concentric Multi-Sphere SOMs has been expanded and generalized into a unified framework in order to thoroughly test the learning capabilities. It is found that Quantization and Topological Error are not suitable to test the learning performance of the algorithms and it is suggested that future work focus on developing new error measures and learning algorithms.",
keywords = "Concentric multi-sphere SOM (CSM-SOM), Quantization error, Spherical SOM, Time series, Topological error",
author = "Tom Gedeon and Lachlan Paget and Dingyun Zhu",
year = "2013",
doi = "10.1007/978-3-642-42042-9_94",
language = "English",
isbn = "9783642420412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "761--768",
booktitle = "Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings",
edition = "PART 2",
note = "20th International Conference on Neural Information Processing, ICONIP 2013 ; Conference date: 03-11-2013 Through 07-11-2013",
}