Textual Documents Clustering Based on Global Keyword Vector Generation and Euclidean Distance
نویسنده
چکیده
Documents clustering are an important task in digital data base of textual documents. Manual clustering of text documents is very tedious and time consuming and labour intensive as well. The keywords vector set determine the vicinity of documents among each other in a given data base. In the presented algorithm, the keywords are extracted from the documents based on the font style, frequency of words and their synonyms. The same step is iterated for each document in the data base and a collective matrix vector of keywords is generated. Now the different columns of the matrix represents individual documents keywords, The Euclidean distance among each column and based on minimum or threshold Euclidean distance, the documents are clustered into different clusters. This gives an adaptive approach in computing the no. of clusters as the no. of clusters is not made as input the system algorithm,
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