Titlebook: Spatial Data and Intelligence; 5th China Conference Xiaofeng Meng,Xueying Zhang,Chunju Zhang Conference proceedings 2024 The Editor(s) (if

grenade

An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete ased on improved density-based spatial clustering of applications with noise (DBSCAN). Experimental results on the dataset of vessels entering and leaving the Taiwan Strait in November 2017 demonstrate the effectiveness of our method.

落叶剂

Mining Regional High Utility Co-location Pattern based on fuzzy density peak clustering. Then, the regional high utility co-location pattern is defined, and an efficient algorithm for mining the patterns in local regions is presented by pruning unpromising patterns. The experiment results show the patterns are meaningful and the mining algorithm is efficient.

长处

0302-9743 anjing, China, during April 25–27, 2024...The 25 full papers included in this book were carefully reviewed and selected from 95 submissions. They were organized in topical sections as follows: Spatiotemporal Data Analysis, Spatiotemporal Data Mining, Spatiotemporal Data Prediction, Remote Sensing Da

迅速飞过

Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networnt information in the features. In addition, we design a structure and semantic contrastive learning module to obtain more comprehensive information about the nodes. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed SSCHC method compared with the state-of-the-art baselines.

变形

RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Estimate Shortest Path Dista In order to verify the validity of the model and sampling scheme, experiments were carried out on four real road network datasets and compared with existing relevant models. The results show that the proposed model has high estimation accuracy, and the training time of the model is nearly 4 times lower than that of the existing baseline model.

彻底明白

STEER

Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-encoderst, so the strategy for constructing the graph is crucial for the performance of the subsequent tasks. In the subsequent comparison task, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To thi

帽子

Cloud-Edge Collaborative Continual Adaptation for ITS Object Detectionces is significant. This issue primarily arises from environmental changes and shifts in data distribution. The problem is twofold: the limited computational capacity of edge devices, which hinders timely model updates, and the inherent limitations in the generalization capabilities of lightweight m

RAFF

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