Competing Behavior of Two Kinds of Self-Organizing Maps and Its Application to Clustering

@article{Matsushita2007CompetingBO,
  title={Competing Behavior of Two Kinds of Self-Organizing Maps and Its Application to Clustering},
  author={Haruna Matsushita and Yoshifumi Nishio},
  journal={IEICE Trans. Fundam. Electron. Commun. Comput. Sci.},
  year={2007},
  volume={90-A},
  pages={865-871}
}
The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOM for nonuniform input data is investigated. Furthermore, we show its application to… Expand
Tentacled Self-Organizing Map for Effective Data Extraction
  • H. Matsushita, Y. Nishio
  • Computer Science
  • The 2006 IEEE International Joint Conference on Neural Network Proceedings
  • 2006
TLDR
A method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction, which possesses both abilities of nSOM and PSOM, and can confirm that TSOM successfully extracts clusters even in the case that the authors do not know the number of clusters in advance. Expand
Tentacled Self-Organizing Map for Effective Data Extraction
TLDR
This study proposes a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that the number of clusters in advance is unknown. Expand
Image Description Using Scale-Space Edge Pixel Directions Histogram
TLDR
A semantic annotation, based on this low level descriptor that results from the multiscale image analysis, will be extracted and improved classification using the nearest class mean and neural networks will be used. Expand
Semantic content ranking through collaborative and content clustering
TLDR
This paper reports how Self-Organising Neural Networks (SONNs) are used to cluster and rank the video segments through consideration of user preferences and knowledge gained from usage of the same content by similar users and of similar content by the same user. Expand
Just-in-Time Personalisation of Real-Time Media Content
  • Ivor T. Walker
  • Computer Science
  • Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)
  • 2007
TLDR
This doctoral research aims to construct a platform that enables just-in-time content modelling and adaptation for real-time streamed media that utilizes normative tools from MPEG-7 part 5 and MPEG-21 part 7. Expand
Just-in-Time Personalisation of Real-Time Media Content
TLDR
This doctoral research aims to construct a platform that enables just-in-time content modelling and adaptation for real-time streamed media that utilizes normative tools from MPEG-7 part 5 and MPEG-21 part 7. Expand

References

SHOWING 1-9 OF 9 REFERENCES
Clustering of the self-organizing map
TLDR
The two-stage procedure--first using SOM to produce the prototypes that are then clustered in the second stage--is found to perform well when compared with direct clustering of the data and to reduce the computation time. Expand
Self-Organizing Maps
  • T. Kohonen
  • Computer Science
  • Springer Series in Information Sciences
  • 1995
TLDR
The mathematical preliminaries, background, basic ideas, and implications of the Self-Organising Map algorithm are expounded in a manner which is accessible without prior expert knowledge. Expand
Clustering with competing self-organizing maps
  • Y. Cheng
  • Computer Science
  • [Proceedings 1992] IJCNN International Joint Conference on Neural Networks
  • 1992
TLDR
The discovery process for the number of clusters is studied and compared to k-means clustering, and the frequency of a clusters outcome is used as a measure of the validity of the clustering. Expand
M2dSOMAP: clustering and classification of remotely sensed imagery by combining multiple Kohonen self-organizing maps and associative memory
  • W. Wan, D. Fraser
  • Computer Science
  • Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan)
  • 1993
This paper investigates a hybrid neural network framework by combining unsupervised and supervised neural learning paradigms on a unified representation platform of multiple Kohonen 2DExpand
Unsupervised speaker recognition based on competition between self-organizing maps
We present a method for clustering the speakers from unlabeled and unsegmented conversation (with known number of speakers), when no a priori knowledge about the identity of the participants isExpand
An Algorithm for Vector Quantizer Design
An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of theExpand
Simulation results of Iris data. (a) Horizontal axis is the sepal length and vertical axis is the petal length. (b) Horizontal axis is the petal width and vertical axis is the sepal width
  • Iris setosa
  • 1995