20,731 research outputs found
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
Methods of Hierarchical Clustering
We survey agglomerative hierarchical clustering algorithms and discuss
efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing maps, and mixture models.
We review grid-based clustering, focusing on hierarchical density-based
approaches. Finally we describe a recently developed very efficient (linear
time) hierarchical clustering algorithm, which can also be viewed as a
hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
Advances in Self Organising Maps
The Self-Organizing Map (SOM) with its related extensions is the most popular
artificial neural algorithm for use in unsupervised learning, clustering,
classification and data visualization. Over 5,000 publications have been
reported in the open literature, and many commercial projects employ the SOM as
a tool for solving hard real-world problems. Each two years, the "Workshop on
Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM
series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has
been successfully organized in 1997 and 1999 by the Helsinki University of
Technology, in 2001 by the University of Lincolnshire and Humberside, and in
2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I
Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in
Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari
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