A self organizing document map algorithm for large scale hyperlinked data inspired by neuronal migration
|A self organizing document map algorithm for large scale hyperlinked data inspired by neuronal migration|
|Author(s)||Nakayama K., Matsuo Y.|
|Published in||Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011|
|Keyword(s)||clustering, link analysis, SOM, visualization, Wikipedia (Extra: Algorithm and implementation, clustering, Data sets, High dimensional data, Hyperlinks, link analysis, MAP algorithms, Neuronal migration, Number and size, Research efforts, Research topics, Self organizing, SOM, SOM algorithms, Technical difficulties, Web document, Web document clustering, Wikipedia, Algorithms, Data visualization, Hypertext systems, Research, Scalability, Self organizing maps, Visualization, World Wide Web, Cluster analysis)|
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A self organizing document map algorithm for large scale hyperlinked data inspired by neuronal migration is a 2011 conference paper written in English by Nakayama K., Matsuo Y. and published in Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011.
Web document clustering is one of the research topics that is being pursued continuously due to the large variety of applications. Since Web documents usually have variety and diversity in terms of domains, content and quality, one of the technical difficulties is to find a reasonable number and size of clusters. In this research, we pay attention to SOMs (Self Organizing Maps) because of their capability of visualized clustering that helps users to investigate characteristics of data in detail. 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 several research efforts 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 a recent 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, and show the practicality of the algorithm by applying MIGSOM to a huge scale real data set: Wikipedia's hyperlink data.
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