The research of Self-Organizing Maps based on Document Collections
Web text mining is a new issue in the knowledge discovery research field.It is aimed to help people discover knowledge from large quantities of semi-structured or unstructured text in the web.Several approaches,including some pure and hybrid information retrieval (IR) methods,have been proposed to tackle such an issue.Among these approaches,combining the Self-Organizing Map (SOM) method with the principles of the vector-space model,appears to be a promising alternative for the traditional purely IR-based methods in this problem domain.The encoded documents are organized on another self-organizing map,a document map,on which nearby locations contain similar documents.Special consideration is given to the computation of very large document maps which is possible with general-purpose computers if the dimensionality of the word category histograms is first reduced with a random mapping method and if computationally efficient algorithms are used in computing the SOMs.
Data mining Document Collections SOM WEBSOM
Yi Ding Xian Fu
The college of computer science and technology Hubei Normal University, Huangshi,China
国际会议
厦门
英文
1232-1235
2012-01-04（万方平台首次上网日期，不代表论文的发表时间）