Earticle

초록

영어

The K-means algorithm is widely used at the initial stage of data analysis in data mining process, partly because of its low time complexity and the simplicity of practical implementation. Cluster validity indices are used along with the algorithm in order to determine the number of clusters as well as the clustering results of datasets. In this paper, we present a performance comparison of sixteen indices, which are selected from forty indices in literature, while considering their applicability to nonhierarchical clustering algorithms. Data sets used in the experiment are generated based on multivariate normal distribution. In particular, four error types including standardization, outlier generation, error perturbation, and noise dimension addition are considered in the comparison. Through the experiment the effects of varying number of points, attributes, and clusters on the performance are analyzed. The result of the simulation experiment shows that Calinski and Harabasz index performs the best through the all datasets and that Davis and Bouldin index becomes a strong competitor as the number of points increases in dataset.

목차

I. 서론
 II. 클러스터링 알고리즘과 인덱스
  2.1 비계층형 클러스터링 알고리즘
  2.2 클러스터링 인덱스
 III. 실험 및 결과
  3.1 시뮬레이션 데이터 생성 기법
  3.2 기본실험
  3.3 확장실험
 IV. 결론 및 향후 연구 방향
 참고문헌

저자

• 심요성 [ Yo-Sung Shim | 고려대학교 산업시스템정보공학과 석사과정 ]
• 정지원 [ Ji-Won Chung | 고려대학교 산업시스템정보공학과 박사과정 ]
• 최인찬 [ In-Chan Choi | 고려대학교 산업시스템정보공학과 교수 ] 교신저자

참고문헌