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Cluster analysis is an important and challenging subject in time series data mining. It has a very important application prospect in many areas, such as medical images, atmosphere, finance, etc. Many current clustering techniques have still many problems, for example, k-means is a very effective method in finding different shapes and tolerating noise, but its result severely depends on the suitable...
This paper presents a novel modular recurrent neural network based on the pipelined architecture (PRWNN) to reduce the computational complexity and improve the performance of the recurrent wavelet neural network (RWNN). The PRWNN inherits the modular architectures of the pipelined recurrent neural network proposed by Haykin and Li and is made up of a number of RWNN modules that are interconnected...
Via analyzing characters of vast disaster news on the internet, a new topic detection algorithm based on Group Average Hierarchical Clustering (GAHC), which is suitable for the processing of big data on the network, is proposed in this paper. The core idea of GAHC is to divide big data into smaller groups, and then cluster groups hierarchically to generate final topics. During the process of clustering,...
The algorithm of line segment intersection is the core algorithm for vector map overlay. The traditional method is based on the plane-sweep process to find the intersections of two groups of lines. Although such method proves to be efficient in serial system, the basic data structure of this kind of algorithm needs to maintain a tree structure, which has the data-lock in multi-core system. In addition,...
Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally...
It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking...
GIS polygon-based (also know as vector-based) spatial data overlay computation is much more complex than raster data computation. Processing of polygonal spatial data files has been a long standing research question in GIS community due to the irregular and data intensive nature of the underlying computation. The state-of-the-art software for overlay computation in GIS community is still desktop-based...
Current trends in network routing point to dynamic routing which provides flexible and efficient routing selections. The usage of dynamic routing protocols significantly promotes the rapid development of Internet. In reality, centralized routing and distributed routing techniques are critical methods to realize dynamic routing in modern communication network. In this paper, we mainly analyze the principles...
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