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avoid unnecessary email reading for that a better email management system is required. Here author used fuzzy logic techniques for email clustering. Extract concept and feature, same feature keyword goes into one cluster if a new keyword is found and not matched with any existing cluster than a new cluster is defined for
mechanisms with a traditional indexing method. The goal is to identify a higher semantic content and more meaningful keyword combinations, considering both supervised and unsupervised techniques. Within a specific implementation both Bayesian learning as well as clustering are integrated to support a boost parameter towards
users to shift through and find relevant information. The information retrievals commonly used are based on keywords. These techniques used keyword lists to describe the content of information, but one problem with such list is that they do not say anything about the symantic relationships between keywords, nor do they
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
using keywords graph to contribute special techniques for exploring those groups and the relationships among them. Interactions between users and the created keywords graph are also provided. Compared to other applications on blog visualization, our approach utilized the ontology knowledge to analysis and automatically
fuzzy Euclidean distance clustering algorithm after using MeSH ontology on medical theses data for better categorization of the keywords within the data.
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