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results in finding a suitable template size that can be used to create tiles for visual keywords. These visual keywords are combined with text keywords to create a multimodal image representation before applying clustering.
This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We
Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combineskeyword selection and
Effectively organizing Web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word
The proliferation of Web services demands for a discovery mechanism to find advertisements that satisfy the requests more accurately. OWL-S provides a capability-based description and logic inference mechanism for semantically matching. UDDI provides a registry of businesses and Web services, but its keyword search
. This repository also contained more than 86.8 million keywords associated with the images. The key contribution of this work is that it combines clustering and natural language processing tasks to automatically create a large corpus of news images with good quality tags or meta-information so that interesting vision tasks
machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy of the subset. Lastly, the taxonomy is modified to the hierarchical structure for user navigation by manual adjustments. The topic digital library is constructed after combining the full-text retrieval and
effectively. This paper proposes a novel personal topics detection approach using clustering algorithm. First preprocess the emails and construct the improved email VSM(vector space model) to label the email combining the body and subject in a new method, then adopt the advanced k-means algorithm to cluster the emails and design
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