Earticle

초록

영어

DBSCAN is a density-based clustering algorithm. This algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency is not high. In order to ensure the high efficiency of DBSCAN clustering algorithm, and reduce its memory footprint. In this paper, the original DBSCAN algorithm was improved, and the G-DBSCAN algorithm is proposed. G-DBSCAN algorithm reduces the number of query object as a starting point. Put the data into the grid, with the center point of the data in the grid to replace all the grid points as the algorithm input. The query object will be drastically reduced, thus improving the efficiency of the algorithm, reduces the memory footprint. In order to make the G-DBSCAN algorithm can adapt to large data processing, we will parallelize the G-DBSCAN algorithm, and combining it with Map Reduce framework. The results prove that G-DBSCAN and MRG-DBSCAN algorithm are feasible and effective.

목차

Abstract
 1. Introduction
 2. Methods
  2.1. DBSCAN Clustering Algorithm
  2.2. The Advantages and Disadvantages of DBSCAN
  2.3. G-DBSCAN Clustering Algorithm
  2.4. G-DBSCAN Algorithm Parallelization (MRG-DBSCAN)
 3. Experimental Analysis
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Li Ma [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, , School of Computer & Software, Nanjing University of Information Science & Technology, Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Lei Gu [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, , School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Bo Li [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, CMA Research Centre for Strategic Development, Beijing 100081 ]
• Shouyi Qiao [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, , School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Jin Wang [ Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, , School of Computer & Software, Nanjing University of Information Science & Technology, Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & Technology, Nanjing 210044 ]

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