원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.6 No.5
2013.10
pp.13-22
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. We compare random method, max average distance method and triangle height method for selecting initial seeds in K-Means algorithm. It reduces total clustering time by minimizing the number of allocation and recalculation.
목차
Abstract
1. Introduction
2. Related Work
2.1. K-Means Algorithm
2.2. Initial Value Setting of K-Means
3. Cluster Center Setting
3.1. Using Max Average Distance
3.2. Using Triangle Height
4. Experiment
5. Conclusion
References
1. Introduction
2. Related Work
2.1. K-Means Algorithm
2.2. Initial Value Setting of K-Means
3. Cluster Center Setting
3.1. Using Max Average Distance
3.2. Using Triangle Height
4. Experiment
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보