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approach. To generate the concept, keywords are extracted from the documents but the extracted set is very large. So for dimensionality reduction, SVD is applied. This paper proposes a novel approach for document clustering based on Formal Concept Analysis (FCA). Concept generation and dimensionality reduction are the two
clustering genes is done in two steps: First, keywords corresponding to all genes of interest from a subset of MEDLINE database were extracted automatically using TF-IDF and Z-scores. In the second step, the classic K-means algorithm was used to group genes into clusters of genes based on the keyword features.
solution helps in reducing the time to write documents by 42% as compared to the traditional methods of writing documents. Sophisticated statistical algorithms along with natural language processing technology are used to continuously determine the keywords and concepts from the content in the document. A web search is
One of the most serious problems that conventional knowledge management (KM) encompasses has been pointed out tardy and ineffective acquisition of knowledge. To resolve this problem, knowledge must be autonomously acquired according to its context of use by applying the technique of keyword extraction in machine
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