Personalized Web Directory
The Web directory is viewed as a concept hierarchy which is generated by a content-based document clustering method. The construction of web community directories is seen here as the end result of a usage mining process the data collected at the proxy servers of the central service on the web. Data Collection and Preprocessing, comprising the collection and cleaning of the data, their characterization using the content of the Web pages, and the identification of user sessions. Note that this step involves a separate data mining process for the discovery of content categories and the characterization of the pages. Pattern Discovery, comprising the extraction of user communities from the data with a suitably extended cluster mining technique, which is able to ascend a thematic hierarchy, in order to discover interesting patterns. Knowledge Post-Processing, comprising the translation of community models into Web community directories and their evaluation. Personalization is realized by constructing community models on the basis of usage data collected by the proxy servers of an Internet Service Provider. For the construction of the community models, a new data mining algorithm, called Community Directory Miner, is used. This is a simple cluster mining algorithm which has been extended to ascend a concept hierarchy, and specialize it to the needs of user communities. 1) To maintain shortcut last releave sub directory. 2) To use personalization in every user. 3) To manipulate personalized data’s. 4) To add open directory for personalized user
PROJECT OUTPUT VIDEO:
The hyper graphical architecture of the Web has been used to support claims that the Web will make Internet-based services really user-friendly. However, at its current state, the Web has not achieved its goal of providing easy access to online information. The manual creation and maintenance of the Web directories leads to limited coverage of the topics that are contained in those directories, since there are millions of Web pages and the rate of expansion is very high. In addition, the size and complexity of the directories is canceling out any gains that were expected with respect to the information overload problem, i.e., it is often difficult for a particular user to navigate to interesting information.
The use of machine learning techniques for modeling the user communities based on clustering and probabilistic modeling. To introduce a novel methodology that combines usage data and thematic information from Web directories. To Methodology provides a promising research direction, where many new issues arise. An analysis regarding the parameters of the community models, such as PLSA, is required. Moreover, additional evaluation on the robustness of the algorithms to a changing environment would be interesting. To achieve this by combining thematic with usage information to model the user communities. To present new versions of the approaches introduced in Web Mining: From Web to Semantic Web and Exploiting Probabilistic Latent Information for the Construction of Community Web Directories and a new method that combines crisp clustering with probabilistic models.
- System : Pentium Dual Core.
- Hard Disk : 120 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 1 GB.
- Operating system : Windows 7.
- Coding Language : NET,C#.NET
- Tool : Visual Studio 2008
- Database : SQL SERVER 2005