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generate and calculate the associated relations and their strengths between documents within a domain. Each document is represented by a bag of words and their weights. We first build domain knowledge background based on the association rules at keyword level, and then we apply those association rules to generate and
discovering pattern as compared to previous text mining methods. The results of the experiment setup show that the keyword-based methods not give better performance than pattern-based method. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the
This paper suggests a SAO network for identifying technological opportunities by reusing inventive knowledge of patents. Despite the keyword-based approach's ease of use and simplicity, the approach is not sufficient for addressing the reuse of technological knowledge because it cannot represent how technological
This paper aims to analyze affective expressions in articles of popular science by text mining with the keywords “Cancer” and “Immunity”. This study selects 145 articles from the website of a magazine and segmented them into 410,919 terms. And the study uses an automatic system to classify the terms into vocabulary
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