loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Abdel-Badeeh M. Salem ; Mostafa M. Syiam and Ayad F. Ayad

Affiliation: Faculty of Computer & Information Sciences, Ain Shams University, Egypt

Keyword(s): Neural networks, Self-Organizing Map, Document Clustering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The Self-Organizing Map (SOM) has shown to be a stable neural network model for high- dimensional data analysis. However, its applicability is limited by the fact that some knowledge about the data is required to define the size of the network. In this paper the Growing Hierarchical SOM (GHSOM) is proposed. This dynamically growing architecture evolves into a hierarchical structure of self–organizing maps according to the characteristics of input data. Furthermore, each map is expanded until it represents the corresponding subset of the data at specific level. We demonstrate the benefits of this novel model using a real world example from the document-clustering domain. Comparison between both models (SOM & GHSOM) was held to explain the difference and investigate the benefits of using GHSOM.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.218.2.191

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
M. Salem, A.; M. Syiam, M. and F. Ayad, A. (2004). UNSUPERVISED ARTIFICIAL NEURAL NETWORKS FOR CLUSTERING OF DOCUMENT COLLECTIONS. In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-00-7; ISSN 2184-4992, SciTePress, pages 383-392. DOI: 10.5220/0002595203830392

@conference{iceis04,
author={Abdel{-}Badeeh {M. Salem}. and Mostafa {M. Syiam}. and Ayad {F. Ayad}.},
title={UNSUPERVISED ARTIFICIAL NEURAL NETWORKS FOR CLUSTERING OF DOCUMENT COLLECTIONS},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2004},
pages={383-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002595203830392},
isbn={972-8865-00-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - UNSUPERVISED ARTIFICIAL NEURAL NETWORKS FOR CLUSTERING OF DOCUMENT COLLECTIONS
SN - 972-8865-00-7
IS - 2184-4992
AU - M. Salem, A.
AU - M. Syiam, M.
AU - F. Ayad, A.
PY - 2004
SP - 383
EP - 392
DO - 10.5220/0002595203830392
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic