To reduce the computational complexity and running time of large‐scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to ...
In this research paper, we present some of the grid based methods such as CLIQUE (CLustering In QUEst) [2], STING (STatistical INformation Grid) [3], MAFIA (Merging of Adaptive Intervals Approach to Spatial Data Mining) [4], Wave Cluster [5]and O-CLUSTER (Orthogonal partitioning CLUSTERing...
Grid-based clustering is particularly appropriate to deal with massive datasets. The principle is to first summarize the dataset with a grid representation, and then to merge grid cells in order to obtain clusters. All previous methods use grids with hyper-rectangular cells. In this paper we prop...
The study in93introduces a stochastic blockchain-based energy management system that utilizes vehicle-to-grid (V2G) and vehicle-to-storage (V2S) technologies to optimize energy distribution in smart cities. The integration of blockchain ensures secure and transparent transactions, enhancing the reliabili...
Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) ...
Hybrid-fl: cooperative learning mechanism using non-iid data in wireless networks. CoRR. 2019. Briggs C, Fan Z, Andras P. Federated learning with hierarchical clustering of local updates to improve training on non-iid data. 2020. arXiv:2004.11791 [CoRR abs]. Wang H, Kaplan Z, Niu D, Li...
In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our ...
Hierarchical clustering algorithms have a great advantage in the analysis of the hierarchical relationship among data clusters (Ward, 1963) with similar data points or clusters as a metric. In this study, we proposed an overlapping index based on the grid density to define the overlapping ...
A non-spatial account of place and grid cells based on clustering models of concept learning. Nat. Commun. 10, 5685 (2019). Article PubMed PubMed Central CAS Google Scholar Savelli, F., Yoganarasimha, D. & Knierim, J. J. Influence of boundary removal on the spatial representations of ...
while using K-Means clustering and PCA to analyze the clusters' architectural and structural characteristics. As a result of this approach, the study reveals that an optimized solution based on the Iranian Chahar-Lengeh Girih pattern significantly improves displacement and elastic energy by ove...