arXiv:1508.04904
공식 코드 · Papers with Code
Review and Perspective for Distance Based Trajectory Clustering
원문 초록
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Stable Trajectory Clustering: An Efficient Split and Merge Algorithm
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.