Earticle

초록

영어

Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. A density-based notion of clusters which is designed to discover clusters of arbitrary shape. In this paper we propose a combined approach based on Minimum Spanning Tree based clustering and Density-based clustering for noise-free high density best number of clusters. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The algorithm works in two phases. The first phase of the algorithm produces subtrees (noise-free clusters). The second phase finds high density clusters from the subtrees.

목차

Abstract
 1. Introduction
 2. Related work
 3. MSTDBCNFHDC Algorithm
 4. Conclusion
 References

키워드

저자

• T. Karthikeyan [ Department of Computer Science, PSG College of Arts and Science, Coimbatore, Tamil Nadu, India ]
• S. John Peter [ Department of Computer Science and Research Center St. Xavier’s College, Palayamkottai, Tamil Nadu, India. ]
• S.Chidambaranathan [ Department of MCA St. Xavier’s College, Palayamkottai, Tamil Nadu, India. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605