MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration
|MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration|
|Author(s)||Nakayama K., Matsuo Y.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Clustering, Link Analysis, SOM, Visualization, Wikipedia (Extra: Flow visualization, Hypertext systems, Learning algorithms, Scalability, Self organizing maps, Visualization, Websites, Algorithm and implementation, Clustering, Link analysis, Scalability and performance, SOM, SOM(self organizing map), Unsupervised machine learning, Wikipedia, Database systems)|
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MIGSOM: A SOM algorithm for large scale hyperlinked documents inspired by neuronal migration is a 2014 conference paper written in English by Nakayama K., Matsuo Y. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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