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
- 2019.6
- 수록면
- 141 - 148 (8page)
- DOI
- 10.33162/JAR.2019.06.19.2.141
이용수
초록· 키워드
오류제보하기
Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the patterns of data for clustering categorical data
Methods: The proposed categorical geodesic distance is calculated with three main steps: (1) The first step measures the Gower distance between two observations composed of categorical variables. (2) The second step is to represent the data as a mutual k-nearest neighbor graph. (3) The final step calculates the distance between two observations with the shortest path in the graph. The distance obtained from (3) is utilized for clustering categorical data. In particular, our proposed method is suitable for data with nonlinear patterns.
Results: Our experimental results using several real-life datasets reveal that the categorical data also has implicit topological structures and confirm that the distance considering implicit data patterns generally yields better clustering performance than existing Gower distance measure.
Conclusion: This study revealed that the adoption of the data patterns using our proposed distance measure positively affected the results of cluster analysis.
Methods: The proposed categorical geodesic distance is calculated with three main steps: (1) The first step measures the Gower distance between two observations composed of categorical variables. (2) The second step is to represent the data as a mutual k-nearest neighbor graph. (3) The final step calculates the distance between two observations with the shortest path in the graph. The distance obtained from (3) is utilized for clustering categorical data. In particular, our proposed method is suitable for data with nonlinear patterns.
Results: Our experimental results using several real-life datasets reveal that the categorical data also has implicit topological structures and confirm that the distance considering implicit data patterns generally yields better clustering performance than existing Gower distance measure.
Conclusion: This study revealed that the adoption of the data patterns using our proposed distance measure positively affected the results of cluster analysis.
목차
1. 서론
2. 범주형 자료를 위한 그래프 기반 거리: 범주형 지오데식 거리(Categorical Geodesic Distance)
3. 실험 계획
4. 결론 및 시사점
References
참고문헌 (20)
참고문헌 신청
김종민 / 2018 / Anomaly Detection in Sensor Data / 신뢰성 응용연구 18(1):20~32
심홍기 / 2018 / 딥러닝을 활용한 엔진 조기점화 패턴 탐색 / 신뢰성 응용연구 18(4):281~290
Lee, J. H. / 2006 / Computing reliability cluster-based in wireless distributed sensor networks / Journal of Applied Reliability 6(4):297~306
Yu, J. / 2016 / Density-based geodesic distance for identifying the noisy and nonlinear clusters / Information Sciences 360(10):231~243
Feil, B. / 2007 / Soft Computing in Industrial Applications / Springer :50~59
심홍기 / 2018 / 딥러닝을 활용한 엔진 조기점화 패턴 탐색 / 신뢰성 응용연구 18(4):281~290
Lee, J. H. / 2006 / Computing reliability cluster-based in wireless distributed sensor networks / Journal of Applied Reliability 6(4):297~306
Yu, J. / 2016 / Density-based geodesic distance for identifying the noisy and nonlinear clusters / Information Sciences 360(10):231~243
Feil, B. / 2007 / Soft Computing in Industrial Applications / Springer :50~59
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UCI(KEPA) : I410-ECN-0101-2019-323-000903312