K-Means is a widely used partition based clustering algorithm famous for its simplicity and speed. It organizes input dataset into predefined number of clusters. K-Means has a major limitation -- the number of clusters, K, need to be pre-specified as an input. Pre-specifying K in the K-Means algorithm sometimes becomes difficult in absence of thorough domain knowledge, or for a new and unknown dataset. This limitation of advance specification of cluster number can lead to “forced” clustering of data and proper classification does not emerge. In this paper, a new algorithm based on the K-Means is developed. This algorithm has advance features of intelligent data analysis and automatic generation of appropriate number of clusters. The clusters generated by the new algorithm are compared against results obtained with the original K-Means and various other famous clustering algorithms. This comparative analysis is done using sets of real data.
목차
Abstract 1. Introduction 2. Related Work 3. An Extended K-Means Algorithm 4. Illustrative Examples 5. Results and Discussion 6. Conclusion References
키워드
ClusteringK-MeansAutomatic generation of clusters
저자
Anupama Chadha [ Faculty of Computer Applications, MRIU, Faridabad, India ]
Suresh Kumar [ Faculty of Engineering and Technology, MRIU, Faridabad, India ]
보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Grid and Distributed Computing
간기
격월간
pISSN
2005-4262
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.9 No.11