Result: Unsupervised learning of image recognition with neural society for clustering

Title:
Unsupervised learning of image recognition with neural society for clustering
Authors:
WOJNARSKI, Marcin
Source:
Rough sets and current trends in computing (5th International conference, RSCTC 2006, Kobe, Japan, November 6-8, 2006)Lecture notes in computer science. :862-871
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 9 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Algorithme k moyenne, K means algorithm, Algoritmo k media, Analyse amas, Cluster analysis, Analisis cluster, Apprentissage non supervisé, Unsupervised learning, Biologie, Biology, Biología, Classification, Clasificación, Economie, Economy, Economía, Entropie, Entropy, Entropía, Fouille donnée, Data mining, Busca dato, Information mutuelle, Mutual information, Información mutual, Interprétation image, Image interpretation, Interpretación imágen, Localité, Locality, Raisonnement basé sur cas, Case based reasoning, Razonamiento fundado sobre caso, Reconnaissance image, Image recognition, Reconocimiento imagen, Sociologie, Sociology, Sociología, Théorie ensemble approximatif, Rough set theory
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Warsaw University, Faculty of Mathematics, Informatics and Mechanics ul. Banacha 2, 02-097 Warszawa, Poland
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19078924
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19078924
Database:
PASCAL Archive

Further Information

New algorithm for partitional data clustering is presented, Neural Society for Clustering (NSC). Its creation was inspired by hierarchical image understanding, which requires unsupervised training to build the hierarchy of visual features. Existing clustering algorithms are not well-suited for this task, since they usually split natural groups of patterns into several parts (like k-means) or give crisp clustering. Neurons comprising NSC may be viewed as a society of autonomous individuals, proceeding along the same simple algorithm, based on four principles: of locality, greediness, balance and competition. The same principles govern large groups of entities in economy, sociology, biology and physics. Advantages of NSC are demonstrated in experiment with visual data. The paper presents also a new method for objective and quantitative comparison of clustering algorithms, based on the notions of entropy and mutual information.