Earticle

초록

영어

Data Stream is a continuous set of data records. When data arrive at a very high speed and continuously, so predicting the class in timely manner is important. Class prediction of data Stream is an important task in data mining. Nowadays Ensemble Modeling technique growing rapidly in Data Stream Classification. Ensemble learning become popular because of its advantage to handle large quantity of data stream, means it can handle the data in a bulk and also it can handle concept drifting. Earlier studies, mostly focused on accuracy of ensemble model, prediction efficiency has not considered much because existing ensemble model predicts in linear time, which is enough for general or small applications and existing models works on integrating small number of classifier. But real world application have large volume of data stream so we need more base classifier to identify different patterns and build a high grade ensemble model. To overcome these challenge we propose height balanced tree indexing structure (Ensemble tree) of base classifier for fast prediction on data streams by ensemble modeling technique. Ensemble Tree handles ensembles as spatial databases and it make use of an R-tree like structure to achieve sub linear time complexity.

목차

Abstract
 1. Introduction
 2. Literature Survey
 3. Motivation
 4. Proposed System
  4.1. Search Operation
  4.2. Insert Operation
  4.3. Delete Operation
 5. Conclusion
 References

키워드

저자

• Shashi [ Department of Information Technology, Bharati Vidyapeeth Deemed University College of Engineering, Vignan’s Institute of Information Technology ]
• Priyanka Paygude [ Department of Information Technology, Bharati Vidyapeeth Deemed University College of Engineering, Vignan’s Institute of Information Technology ]
• Snehal Chaudhary [ Department of Information Technology, Bharati Vidyapeeth Deemed University College of Engineering, Vignan’s Institute of Information Technology ]
• Debnath Bhattacharyya [ Department of Information Technology, Bharati Vidyapeeth Deemed University College of Engineering, Vignan’s Institute of Information Technology ]
• Hye-jin Kim [ Business Administration Research Institute, Sungshin W. University Pune-411043, Maharashtra, India ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605