Earticle

초록

영어

Clustering is the most used technique in data mining. Clustering maximize the intra-cluster similarity and minimize the inter clusters similarity. DBSCAN is the basic density based clustering algorithm. Cluster is defined as regions of high density are separated from regions that are less dense. DBSCAN algorithm can discover clusters of arbitrary shapes and size in large spatial databases. Beside its popularity, DBSCAN has drawbacks that its worst time complexity reaches to O (n2). Similarly, it cannot deal with varied densities. It is hard to know the initial value of input parameters. In this study, we have studied and discussed some significant enhancement of DBSCAN algorithm to tackle with these problems. We analysed all the enhancements to computational time and output to the original DBSCAN. Majority of variations adopted hybrid techniques and use partitioning to overcome the limitations of DBSCAN algorithm. Some of which performs better and some have their own usefulness and characteristics.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Critical Analysis
 4. Conclusion and Future Work
 References

키워드

저자

• Said Akbar [ Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan ]
• M.N.A. Khan [ Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan ]

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