SEARCH OF CATEGORIZED INTERNET CONTENT BASED ON CONSUMPTION ANALYSIS IN THE SOCIAL NETWORK OF A USER
Published In: SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, ELECTRICAL AND COMPUTER ENGINEERING
Author(s): BHANU LUTHRA , MANDEEP KAUR
Abstract: In Today’s World, the consumption of internet content has increased manifold. The number of people as well as number of websites has increased exponentially. Therefore there is a big need for performing a consumption analysis of this internet consumption. There are various applications to track this consumption at individual levels, but none to do it for a closed group like a family or a class or a project team. Our work proposes an application which not only tracks the internet consumption of the individuals but also collaborates it for a social group and displays the summary in a categorical format. Such an application will club the internet content visited and consumed by a group of people connected together by their own choice and categorizes the recorded data into various heads based on type as well as level. The result of this application can be customized on the basis of inputs given by a user, providing a very crisp summary which has a plethora of practical advantages to variou
- Publication Date: 13-Jun-2013
- DOI: 10.15224/978-981-07-6935-2-71
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CLASSIFICATION OF STREAM DATA IN THE PRESENCE OF DRIFTING CONCEPT
Published In: SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, ELECTRICAL AND COMPUTER ENGINEERING
Author(s): L.G. MALIK , SNEHLATA S. DONGRE , SUDHIR RAMRAO RANGARI
Abstract: Mining stream data have recently garnered a great deal of attention for Machine Learning Researcher. The major challenges in stream data mining are drifting concept that deals with data whose nature changes over time. Concept drift is one of the core problems in Stream data mining and machine learning. Classification of Stream data in the presence of drifting concepts is more difficult and one of the core issue. In this paper, A Classifier based on hybrid approach is proposed and implemented that handle concept drifting stream data. The proposed classifier is used Naives Bayes as base learner for classification of concept drifting stream data where as concept drift is detected and handled by using drift detection method. Experiments and results on datasets show that the proposed approach performs well with improvement in accuracy of classification and can detect and adapt to concept drifts.
- Publication Date: 13-Jun-2013
- DOI: 10.15224/978-981-07-6935-2-72
- Views: 0
- Downloads: 0