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In view of the problems of the existing keyword extraction algorithm, such as large amount of computation and complex calculation process, this paper proposes an algorithm based on FP-Growth to extract keyword from Chinese documents. The FP-Growth algorithm mines word co-occurrence information, excluding the
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
approach employs a well-known text-mining technique that extracts keywords using TF-IDF. The analysis is based on keywords from the course materials matching to the keywords from online documents, which is similar to the domain expert. Moreover, a new measurement is proposed to quantify associations between course materials
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.
. A third technique involves extraction of keywords and storing them in a properly indexed base. These then can serve the dual purpose of providing solutions to Lazy Learning classification for automatic subject-wise archiving and formation of relevant word sequences for detection of plagiarism using Association Rule
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