Self Organizing Maps for Visualization of Categories
نویسندگان
چکیده
Visualization of Wikipedia categories using Self Organizing Maps shows an overview of categories and their relations, helping to narrow down search domains. Selecting particular neurons this approach enables retrieval of conceptually similar categories. Evaluation of neural activations indicates that they form coherent patterns that may be useful for building user interfaces for navigation over category structures.
منابع مشابه
EM Algorithms for Self-Organizing Maps
Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive EM algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional dat...
متن کاملVisualization of Object Oriented Software Measures using Self-Organizing Maps
Role of self-organizing maps in visualization and analysis of software measures is presented and discussed in this paper. We reveal how self-organizing maps can create a user-friendly and interactive visualization tool that helps software designer to inspect various alternatives and get a thorough insight into the structure of the clusters of the software modules and related metrics. We show ho...
متن کاملInsights Gained by Visualization of a Wireless Channel Output using Self-Organizing Maps
The self-organizing map is an algorithm which can be used to establish the presence of various categories in a given numerical and non-numerical data and to visualize the complex topographical relations between the various such categories in a low dimensional and easily comprehensible display. In this paper, SOM has been used to visualize the output of a wireless channel in the presence of inte...
متن کاملESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM
An overview on the usage of emergent self organizing maps is given. U-Maps visualize the distance structures of high dimensional data sets. P-Maps show their density structures and U*-Maps combine the advantages of the mentioned maps to a visualization suitable to detect nontrivial cluster structures. A concise summary on the usage of Emergent Self-organizing Maps (ESOM) for data mining is give...
متن کاملThe self-organizing map in synoptic climatological research
Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar tomostother clustering methods.However, as the results froma...
متن کامل