Data Stream Clustering Algorithm Based on Neighborhood Covering
Data stream clustering analysis is one of the key techniques in data stream mining.To meet the requirement of evolution and high-speed processing,a data stream clustering algorithm based on Neighborhood Covering is proposed,namely NCStream.By building Neighborhood Covering model,the proposed algorithm for the evolving procedure of data stream is defined and analyzed at length,including the adjustment,creation,deletion and mergence of covering cluster,and simultaneously maintain the cluster feature online.Compared with the similar clustering method,NCStream has no assignment in the number of cluster in advance,which avoids the disadvantage of clustering result due to parameter setting.Moreover,NCStream benefits the establishment of spatial index.Hence,the evolution of data stream is more effectively reflected.The experimental results on real wireless monitor data sets demonstrate that NCStream is of better performance in clustering shape,quality and processing time.
We present a computer simulation study of thin crystalline multilayers constructed from two FCC solids with differing lattice constants and binding energies. Both materials are described by Lennard-Jones interatomic potentials and initially have the same orientation and coherent interfaces. We have studied systems in which interfaces are perpendicular to the common [100] and [111] directions, respectively. A novel technique for analyzing local atomic ordering, Common Neighbor Analysis, is used to identify structural characteristics in these systems. We have found several structural changes in the layers of smaller atoms, including an FCC to HCP transition. In a system with (111) texture, a coherent interface to incoherent interface transformation is observed. Calculations of elastic constants of these multilayer structures show that elastic anomalies are associated with the structural variations.