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This paper proposes a method that can reliably monitor the adoption of existing technology by term frequency-inverse document frequency (TF-IDF) and K-means clustering using cited patents. TF-IDF and K-means clustering can extract patent information when the number of patents is sufficiently large. When the number of patents is too small for TF-IDF and K-means clustering to be reliable, the method...
Keywords are indexed automatically for large-scale categorization corpora. Indexed keywords of more than 20 documents are selected as seed words, thus overcoming subjectivity of selecting seed words in clustering; at the same time, clustering is limited to particular category corpora and keywords indexed feature
Document clustering is to group documents according to a certain semantic features defined on the document set for measuring the similarities between two documents. The keyword models such as the TFIDF model of document have been widely used as features for document clustering. But it lacks of semantic structure
vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of
Finding information based on an object's profile is very useful when exact keywords for the object are unknown. Current image retrieval system all ignores the color information, for example we want to find a super-star with a piece of red petticoat, or we want to a red flower with white background. They all cannot
Web has grown to a huge mass of information resource and is diverse in content. To search such rich source of information one has to be very precise in using keywords in queries to retrieve the relevant documents. Most of the queries issued to search engines are short and have ambiguous context. One way to produce
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