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User preference tree based personalized online learning managment system

Author: 
Nandhini, C, Dr. Vimala Devi, M. and Suriya Gubendiran, K.,
Subject Area: 
Physical Sciences and Engineering
Abstract: 

Intelligent decision is the key technology of smart systems. Data mining technology has been very important in decision-making activities. A Frequent item set mining (FIM), as an important step of association rule analysis is becoming one of the most important research fields in data mining. Weighted FIM in uncertain databases should take both existential probability and importance of items into account in order to find frequent itemsets of great importance to users. This system is intended to show the things occurred in between the searches happened in the place of client and server. The users can able to know about the process of sending ahttp request for the particular thing and getting a http response for that request. But no one can able find out the internal process of searching thousands of records from a large database. This system openlyvisible the internal process of the searching. The WD-FIM is failed to deliver the document based on their preference because the preference should be of any type like pdf, ppt, word document, text document, image and video To overcome the problem of WD-FIM it should be combined with User preference tree. This is a web based online project. The main aim of the project is a providing Learning Course to the user based on their preference. The algorithm should learn the user behavior and provide the preference type in ascending order. Many learning resource management system can offer basic course administration features, but their functionality isn't as robust. It also typically use users behavior to track learners competencies and recommend materials, but most systems lack the capability to deliver personalized materials to the user. Ex: Learning Resource Management should provide authoring, sequencing, and aggregation tools that structure content to facilitate the learning process.

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