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
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.5 No.4
- 발행연도
- 2011.12
- 수록면
- 346 - 353 (8page)
이용수
초록· 키워드
오류제보하기
With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to tit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROBLEM DEFINITION : BOOLEAN+CLUSTERING
IV. DISTANCE MEASURE
V. SEMCLUSTER ALGORITHM
VI. EXPERIMENTAL EVALUATION
VII. CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2013-569-001213113