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MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration
Abstract The SOM (Self Organizing Map), one of the The SOM (Self Organizing Map), one of the most popular unsupervised machine learning algorithms, maps high-dimensional vectors into low-dimensional data (usually a 2-dimensional map). The SOM is widely known as a "scalable" algorithm because of its capability to handle large numbers of records. However, it is effective only when the vectors are small and dense. Although a number of studies on making the SOM scalable have been conducted, technical issues on scalability and performance for sparse high-dimensional data such as hyperlinked documents still remain. In this paper, we introduce MIGSOM, an SOM algorithm inspired by new discovery on neuronal migration. The two major advantages of MIGSOM are its scalability for sparse high-dimensional data and its clustering visualization functionality. In this paper, we describe the algorithm and implementation in detail, and show the practicality of the algorithm in several experiments. We applied MIGSOM to not only experimental data sets but also a large scale real data set: Wikipedia's hyperlink data.real data set: Wikipedia's hyperlink data.
Abstractsub The SOM (Self Organizing Map), one of the The SOM (Self Organizing Map), one of the most popular unsupervised machine learning algorithms, maps high-dimensional vectors into low-dimensional data (usually a 2-dimensional map). The SOM is widely known as a "scalable" algorithm because of its capability to handle large numbers of records. However, it is effective only when the vectors are small and dense. Although a number of studies on making the SOM scalable have been conducted, technical issues on scalability and performance for sparse high-dimensional data such as hyperlinked documents still remain. In this paper, we introduce MIGSOM, an SOM algorithm inspired by new discovery on neuronal migration. The two major advantages of MIGSOM are its scalability for sparse high-dimensional data and its clustering visualization functionality. In this paper, we describe the algorithm and implementation in detail, and show the practicality of the algorithm in several experiments. We applied MIGSOM to not only experimental data sets but also a large scale real data set: Wikipedia's hyperlink data.real data set: Wikipedia's hyperlink data.
Bibtextype inproceedings  +
Doi 10.1007/978-3-319-05810-8_6  +
Has author Kotaro Nakayama + , Yutaka Matsuo +
Has extra keyword Flow visualization + , Hypertext systems + , Learning algorithms + , Scalability + , Self organizing maps + , Visualisation + , Websites + , Algorithm and implementation + , Clustering + , Link analysis + , Scalability and performance + , SOM + , SOM(self organizing map) + , Unsupervised machine learning + , Wikipedia + , Database systems +
Has keyword Clustering + , Link analysis + , SOM + , Visualisation + , Wikipedia +
Issn 3029743  +
Issue PART 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 79–94  +
Published in Lecture Notes in Computer Science +
Title MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration +
Type conference paper  +
Volume 8421 LNCS  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 15:59:04  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 15:59:04  +
DateThis property is a special property in this wiki. 2014  +
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MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration + Title
 

 

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