빅데이터에서 개선된 TI-FCM 클러스터링 알고리즘
Improved TI-FCM Clustering Algorithm in Big Data
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초록
FCM 알고리즘은 반복 최적화 기법을 통해 최적해를 찾는다. 특히, 클러스터링 초기 중심과 잡음의 위치, 몰려있는 밀도의위치, 개수에 따라 실행시간 차이가 난다. 하지만 이 방법은 중심점을 점차 갱신해 나가는 방법으로 초기 클러스터 중심이한 쪽으로 치우치게 되고 클러스터링 결과의 편차가 심해 클러스터링 대푯값의 신뢰도가 떨어진다. 따라서 본 논문에서는 삼각부등식을 이용하여 클러스터 간 거리를 최대한 멀어지게 하여 클러스터 중심 밀도를 결정하는 TI-FCM(TriangularInequality-Fuzzy C-Means:삼각부등식-FCM)클러스터링 알고리즘을 제안한다. 제안된 방법은 대용량의 빅데이터에서도FCM에 비해 실제 클러스터에 수렴하는 효과적인 방법이고 실험을 통해 기존 FCM보다 실행시간이 감소됨을 보였다
Abstract
The FCM algorithm finds the optimal solution through iterative optimization technique. In particular, there is adifference in execution time depending on the initial center of clustering, the location of noise, the location and numberof crowded densities. However, this method gradually updates the center point, and the center of the initial cluster isshifted to one side. In this paper, we propose a TI-FCM(Triangular Inequality-Fuzzy C-Means) clustering algorithm thatdetermines the cluster center density by maximizing the distance between clusters using triangular inequality. Theproposed method is an effective method to converge to real clusters compared to FCM even in large data sets. Experiments show that execution time is reduced compared to existing FCM.
참고문헌 (10)
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