Document Clustering Using Concept Space and Cosine Similarity Measurement

2009 ◽  
Author(s):  
Lailil Muflikhah ◽  
Baharum Baharudin
Keyword(s):  
Document Clustering ◽  
Cosine Similarity ◽  
Similarity Measurement ◽  
Concept Space

scholarly journals Improving document clustering in a learned concept space

2010 ◽  
Vol 46 (2) ◽  
pp. 180-192 ◽  
Author(s):  
Jean-François Pessiot ◽  
Young-Min Kim ◽  
Massih R. Amini ◽  
Patrick Gallinari
Keyword(s):  
Document Clustering ◽  
Concept Space

scholarly journals Improved sqrt-cosine similarity measurement

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Sahar Sohangir ◽  
Dingding Wang
Keyword(s):  
Cosine Similarity ◽  
Similarity Measurement

scholarly journals Analysis of Document Clustering based on Cosine Similarity and K-Main Algorithms

2019 ◽  
Vol 1 (2) ◽  
pp. 164-177
Author(s):  
Bambang Krismono Triwijoyo ◽  
Kartarina Kartarina
Keyword(s):  
Document Clustering ◽  
Cosine Similarity ◽  
Experimental Results ◽  
Text Documents ◽  
Number Of Clusters ◽  
Efficient Organization

Clustering is a useful technique that organizes a large number of non-sequential text documents into a small number of clusters that are meaningful and coherent. Effective and efficient organization of documents is needed, making it easy for intuitive and informative tracking mechanisms. In this paper, we proposed clustering documents using cosine similarity and k-main. The experimental results show that based on the experimental results the accuracy of our method is 84.3%.

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