Earticle

초록

영어

Distributed data mining (DDM) techniques have become necessary for large and multi-scenario datasets requiring resources, which are heterogeneous and distributed in nature. In this paper, we focus our attention on distributed data mining approach via grid. We have discussed and analyzed a new framework based on grid environments to execute new distributed data mining approaches that best suits a distributed and heterogeneous datasets that are commercially available. The architecture and motivation for the design have also been presented in this paper. A detailed survey on distributed data mining technology was also carried out which could offer a better solution since they are designed to work in a grid environment by paying careful attention to the computing and communication resources.

목차

Abstract
 1. Introduction
  1.1. Distributed Approach in a Database
  1.2. Distributed approach in a trusted Data Warehouse
  1.3. Parallel and distributed data mining
  1.4. Distributed data mining
  1.5. Grid computing as a technique for distributed scenario
 2. Existing Developments of Distributed Data Mining in Data Set
 3. Knowledge Grid
  3.1 Globus Toolkit Services
  3.2 DDM Using Grid Architecture
 4. Comparison of Predictive Apriori and Apriori Algorithms
 5. Future of GRID
 6. Conclusion & Future Work
 References

키워드

저자

• Bagrudeen Bazeer Ahamed [ Assistant Professor, Department Of Information Technology Pavendar Bharathidasan College of Engineering & Technology ]
• Shanmugasundaram Hariharan [ Associate Professor, Department Of Information Technology Pavendar Bharathidasan College of Engineering & Technology ]

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