Fast SNN-based clustering approachfor large geospatial data sets [ M ]Connecting a Digital EuropeThrough Location and Place. [ S, 1. ] : Springer International Publishing,2014:179-195.A. Antunes, M. Y. Santos, and A. Moreira, "Fast SNN-Based Clustering Approach for Large Geospatial Data ...
This chapter presents a clustering method, based on the SNN algorithm that significantly reduces the processing time by segmenting the spatial dimension of the data into a set of cells, and by minimizing the number of cells that have to be visited while searching for the k-nearest neighbours ...
Shared Nearest Neighbor-based Clustering by Fast Search and Find of Density Peaks - liurui39660/SNNDPC
The assumption of infinite main memory is very usual while designing most of the clustering algorithms but this assumption fails when the size of data set starts increasing. In this scenario, data needs to be stored in the secondary memory and time spent in the input/outputs (I/O) dominates...
The malware clustering algorithms commonly used at present have gradually can not adapt to the growing number of malware. In order to improve the malware clustering algorithm, this paper uses the clustering algorithm based on Shared Nearest Neighbor (SNN), and uses frequencies of the system calls ...
Over-samplingAn over-sampling method, SD-CSMOTE, is proposed to address the problem of intra-class imbalance in data. First, the minority samples are clustered via the shared nearest neighbour-density peak clustering method. The sample density within each cluster is then determined using the ...
a tuning data method by data pool filtering and clustering is adopted,as well as a useful data fusion method for multi-sensor system.The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process,which enables a ...
This paper proposes a novel approach for the problem based on DBSCAN (density-based spatial clustering of applications with noise) using SNN (shared nearest neighbor). It generates attractor features of RR-Lorenz with neither prior labels nor human interventions which would be later used to measure...
clusteringhuman mobilityIdentifying clusters from individual origin鈥揹estination (OD) flows is vital for investigating spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non-uniform flow clusters from network-constrained OD flows continues to be a challenge. This study ...
The malware clustering algorithms commonly used at present have gradually can not adapt to the growing number of malware. In order to improve the malware clustering algorithm, this paper uses the clustering algorithm based on Shared Nearest Neighbor (SNN), and uses frequencies of the system calls ...