원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.8 No.1
2015.02
pp.11-22
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A grouping of data objects such that the objects within a group are similar (or related) to one another and different from (or unrelated to) the objects in other groups. Many of clustering algorithm is available to analyze data. This paper intends to study and compare different clustering algorithms. These algorithms include K-Means, Farthest First, DBSCAN, CURE, Chameleon algorithm. All these algorithms are compared on the basis of their pros and cons, similarity measure, their working, functionality and time complexity.
목차
Abstract
1. Introduction
2. Related Work
3. Clustering Algorithms
3.1 K-Means Algorithm
3.2 Farthest First Algorithm
3.3 DBSCAN Algorithm
3.4 CURE Algorithm
3.5 Chameleon Algorithm
4. Comparison of Algorithms
5. Evaluation and Results
6. Conclusion and Future Work
References
1. Introduction
2. Related Work
3. Clustering Algorithms
3.1 K-Means Algorithm
3.2 Farthest First Algorithm
3.3 DBSCAN Algorithm
3.4 CURE Algorithm
3.5 Chameleon Algorithm
4. Comparison of Algorithms
5. Evaluation and Results
6. Conclusion and Future Work
References
저자정보
참고문헌
자료제공 : 네이버학술정보