지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.22 No.1
- 발행연도
- 2023.3
- 수록면
- 63 - 72 (10page)
- DOI
- 10.7232/iems.2023.22.1.063
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초록· 키워드
오류제보하기
Initial solution selection is an important process in determining an optimal solution for the efficient classification of clustering methods in machine learning. This study proposes an improved initialization method for a simple and fast Kmedoids clustering algorithm, considering not only the sum of the relative distance rates for each data point, but also the distance between the previously selected medoids (representative objects). Selection of the balanced initial medoids causes the dissimilarity of patterns in the same cluster to be small and the dissimilarity of patterns in different clusters to be large. The performance of the proposed method is verified and validated based on two valid indices of clustering solutions by employing seven machine learning repository datasets of prominent applications with large improvements. The Mann–Whitney test is employed to assess the statistical significance of the performance differences between the method proposed by Park and Jun (2009) and the initialization method for clustering proposed here. The proposed initial selection method is effective, highly reliable, and computationally practical even for large problems.
목차
ABSTRACT
1. INTRODUCTION
2. DATA-CLUSTERING EVALUATION MODEL
3. INITIAL SOLUTION SELECTION FOR CLUSTERING
4. EXPERIMENTAL ANALYSIS, RESULTS, AND DISCUSSION
5. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2023-530-001330784