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This paper presents a Biomedical Semantic-based Association Rule method that significantly reduces irrelevant connections through semantic filtering. The experiment result shows that compared to traditional association rule-based approach, our approach generates much fewer rules and a lot of these rules represent relevant connections among biological concepts.
We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of...
This paper is dedicated to investigating the value of information from sibling pages for Web page clustering. We use a link-based clustering algorithm to examine the usefulness of sibling links for improving clustering quality. The algorithm is extended by two types of edge weighting techniques. The results of the experiments conducted on WebKB4 dataset prove that: (1) using information from sibling...
Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although itpsilas a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption...
The majority of text retrieval and mining techniques are still based on exact feature (e.g. words) matching and unable to incorporate text semantics. Many researchers believe that the extension with semantic knowledge could improve the results and various methods (most of them are heuristic) have been proposed to account for concept hierarchy, synonymy, and other semantic relationships. However, the...
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