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
- 발행연도
- 2021.9
- 수록면
- 1,121 - 1,133 (13page)
- DOI
- 10.7465/jkdi.2021.32.5.1121
이용수
초록· 키워드
오류제보하기
Most clustering algorithms considering spatial characteristics of data have been developed based on the geological location of observations. Density-based spatial clustering of applications with noise (DBSCAN) provides arbitrarily shaped clusters grouping a set of observations which are closely packed together and noise detecting outliers which lie alone in low-density regions. A distance measure for DBSCAN is Euclidean distance, which is the standard measure of distance and especially suitable to handle continuous variables. To handle both categorical and continuous variables simultaneously, other measures are required to compute distance for various types of variables. Thus, we propose DBSCAN algorithm using Gower distance. We provide numerical results on spatial and non-spatial setup comparing DBSCAN methods with Euclidean and Gower distance and we apply this method to land price data and migraine treatments data. DBSCAN using Gower distance has a reasonable method and gives comparably stable results.
목차
Abstract
1. Introduction
2. Density-based spatial clustering for applications with noise (DBSCAN) using Gower distance
3. Simulation study
4. Example : Land price data, Daegu, Korea
5. Conclusion
References
참고문헌 (18)
참고문헌 신청
Argiento, R. / 2014 / A density-based algorithm for cluster analysis using species sampling Gaussian mixture models / Journal of Computational and Graphical Statistics 23:1126~1142
Ester, M. / 1996 / A density-based algorithm for discovering clusters in large spatial databases with noise / International Conference on Knowledge Discovery and Data Mining 2:226~231
Goncalves, L. S. / 2008 / Comparison of multivariate statistical algorithms to cluster tomato heirloom accessions / Genetics and Molecular Research 7:1289~1297
Gower, J. C. / 1971 / A general coe cient of similarity and some of its properties / Biometrics 27:857~871
Gulanbaier, T. / 2014 / Gower distance-based multivariate control charts for a mixture of continuous and categorical variables / Expert Systems with Applications 41:1701~1707
Ester, M. / 1996 / A density-based algorithm for discovering clusters in large spatial databases with noise / International Conference on Knowledge Discovery and Data Mining 2:226~231
Goncalves, L. S. / 2008 / Comparison of multivariate statistical algorithms to cluster tomato heirloom accessions / Genetics and Molecular Research 7:1289~1297
Gower, J. C. / 1971 / A general coe cient of similarity and some of its properties / Biometrics 27:857~871
Gulanbaier, T. / 2014 / Gower distance-based multivariate control charts for a mixture of continuous and categorical variables / Expert Systems with Applications 41:1701~1707
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