Clustering wih SOM: U*C

WSOM 2005 - 5th Workshop on Self-Organizing Maps

Volumn , Issue , 2005, Pages 75-82

  Ultsch, Alfred ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

Clustering; Density based clustering algorithms; SOM; Visualization

Indexed keywords

CLUSTERING; CLUSTERING PROBLEMS; DENSITY-BASED CLUSTERING ALGORITHMS; DISTANCE INFORMATION; HIER-ARCHICAL CLUSTERING; HIGH DIMENSIONAL DATA; NEW CLUSTERING ALGORITHMS; SOM;

CONFORMAL MAPPING; FLOW VISUALIZATION; VISUALIZATION;

CLUSTERING ALGORITHMS;

EID: 84890307265     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. A.K. Jain, R.C. Dubes (1998): Algorithms for Clustering Data, New York, Wiley.
2. A. Ultsch, C. Vetter(1994), Selforganizing Feature Maps vs. statistical clustering, Dept. of Computer Science University of Marburg, Research Report 9.
3. A. Ultsch, H.P. Siemon (1990), Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis, Proc. Intern. Neural Networks, Kluwer Academic Press, Paris, 305-308.
4. A. Ultsch(2003), Maps for the Visualization of high-dimensional Data Spaces, Proc. WSOM, Kyushu, Japan, 225-230.
5. V. Luc, P. Soille(1991), Watersheds in Digital Spaces: An Efficient Algorithm Based on Im-mersion Simulations, IEEE Transactions of Pattern Analysis and Machine Intelligence, Vol. 13(6), 583-598.
6. A. Ultsch(1999), Data Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series, E. Oja, S. Kaski (eds), Kohonen Maps, 33-46.
7. S. Kaski et al (1999), Analysis and Visualisation of Gene Expression Data using Self Organizing Maps, Proc NSIP, 99-100.
8. *-Matrix: A Tool to visualize Clusters in high dimensional Data, Dept. of Computer Science University of Marburg, Research Report 36.
9. A. Ultsch, L. Herrmann (2005), The architecture of Emergent Self-Organizing Maps to reduce projection errors, ESANN, Brugges 2005, pp 1-6.
10. A. Ultsch, F. Mörchen(2005), ESOM-Maps: tools for clustering, visualization, and classification with ESOM, Dept. of Computer Science Univ. of Marburg, Research Report. 46.