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نویسندگان
چکیده
منابع مشابه
using som neural network in text information retrieval
with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...
متن کاملThe Comparison of SOM and K-means for Text Clustering
SOM and k-means are two classical methods for text clustering. In this paper some experiments have been done to compare their performances. The sample data used is 420 articles which come from different topics. K-means method is simple and easy to implement; the structure of SOM is relatively complex, but the clustering results are more visual and easy to comprehend. The comparison results also...
متن کاملIntegrating contextual information to enhance SOM-based text document clustering
Exploration of text corpora using self-organizing maps has shown promising results in recent years. Topographic map approaches usually use the original vector space model known from Information Retrieval for text document representation. In this paper I present a two stage model using features based on sentence categories as alternative approach which includes contextual information. Algorithmi...
متن کاملA SOM-Based Information Organizer for Text and Video Data
We propose an information organizer for e ective clustering and similarity-based retrieval of text and video data. Instead of giving keywords or authoring them, we use a vector space model and DCT image coding in order to extract characteristics of data. Data are clustered by Kohonen's self-organizing map, and the result is visualized in a 3D form. By this, similarity-based retrieval is achieve...
متن کاملSOM-Based Methodology for Building Large Text Archives
Self-Organizing Maps (SOMs) have recently been used to archive over 7 million documents. Not only have SOMs been shown to scale up to very large document collections, these maps also allow for a novel mode of navigating through a large collection of text documents. The SOM training algorithm is unsupervized in that the category of each training document is not known during training. This featur...
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ژورنال
عنوان ژورنال: Socialvetenskaplig tidskrift
سال: 2016
ISSN: 2003-5624,1104-1420
DOI: 10.3384/svt.2000.7.1-2.2824