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Start Over Subject Self-Organizing Map (SOM)

1. Cluster visualization and nonlinear projection techniques for biological sequences

Creator:
Ferles, C. and Stafylopatis, A.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
non-numerical/symbolical sequence data, DNA, RNA, protein, clustering, visualization, mapping, nonlinear projection, Self-Organizing Map (SOM), and Hidden Markov Model (HMM)
Language:
English
Description:
The present study devises two techniques for visualizing biological sequence data clusterings. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are preserved as faithfully as possible. The resulting unified framework incorporates directly raw symbolical sequence data (without necessitating any preprocessing stage), and moreover, operates on a pure unsupervised basis under complete absence of prior information and domain knowledge.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

2. Identification of type daily diagrams of electric consumption based on cluster analysis of multi-dimensional data by neural network

Creator:
Garlík, Bohumír and Křivan , Miloš
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Artificial Neurqal Network (ANN), Self-Organizing Map (SOM), Kohonen map, counter-propagation neural network, cluster analysis, Type daily Diagram (TDD), load prediction, intelligent building, and micro grif
Language:
English
Description:
This article establishes a mathematical description of a self-organizing neural network used for cluster analysis with a subsequent sampling of its effectiveness as an example of identification of the type daily diagrams of electric energy-consumption of complex intelligent buildings within an electric micro grid, namely for a typical work day and a day off on the basis of its annual history. The mentioned type daily diagram can be used to predict power consumption. This method is given in the context of the commonly used procedure for cluster analysis. The experiment was processed in the computer program Artint © 2010.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public