In view of dynamic changes of the road network, the aim of this essay is to recognize the location of the road with changed restriction information. Based on the FCD(floating car data), it studies the trajectory chara...
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In view of dynamic changes of the road network, the aim of this essay is to recognize the location of the road with changed restriction information. Based on the FCD(floating car data), it studies the trajectory characteristics of the vehicle at first, including when the vehicle turning left, right or around. And then, it extracts the target points by using two criteria, one is the deflection angle of the vehicle, the other one is the change of the relative position. In addition, also the most important step, it determines the initial clustering center according to the density distribution characteristics of target points. Lastly, after clustered by K-means, it can identify quickly that the road allowed to turn or not. And if the traffic information of this road is same as the actual road, the restriction state is unchanged, otherwise, changed. Compared with the traditional algorithms, the accuracy of the optimized one is higher, and it can take advantage of FCD to implement the dynamic update of change information of the urban road network.
In recent years, with the increasingly widespread application of unmanned aerial vehicle (UAV), the network technology of UAV has also caused for concern. In this paper, according to the background of related technolo...
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ISBN:
(纸本)9781467300407
In recent years, with the increasingly widespread application of unmanned aerial vehicle (UAV), the network technology of UAV has also caused for concern. In this paper, according to the background of related technologies of UAV, a mobility prediction clustering algorithm (MPCA) relying on the attributes of UAV is proposed. The dictionary Trie structure prediction algorithm and link expiration time mobility model are applied in this clustering algorithm to solve the difficulty of high mobility of UAV. The simulation shows that the reasonable clusterhead electing algorithm and on-demand cluster maintenance mechanism guarantee the stability of the cluster structure and the performance of the network.
In order to prolong the network lifetime, energy-efficient routing protocols should be designed to adapt the characteristic of wireless sensor networks. clustering algorithm is a kind of key technique used to reduce e...
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ISBN:
(纸本)9783642226939
In order to prolong the network lifetime, energy-efficient routing protocols should be designed to adapt the characteristic of wireless sensor networks. clustering algorithm is a kind of key technique used to reduce energy consumption. which can make a longer life span of sensor network. A clustering algorithm based on timer mechanism (CATM) was presented. By means of timer mechanism, it can guarantee the node with high remaining energy be chosen as the cluster heads node in priority in each round. Meanwhile, a aggregate of nodes that have received cluster heads information is preserved so that the algorithm is able to ensure that obtain a constant number of cluster heads and cluster heads are well scattered. Simulation results demonstrate that CATM algorithm effectively balances the energy dispatch of the sensors and obvious improvement on the network lifetime.
Background: With the rapid development of information technologies, digging out useful information from mass data has become a hot issue. We should cluster the data before the analysis. Human clustering of mass data c...
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Background: With the rapid development of information technologies, digging out useful information from mass data has become a hot issue. We should cluster the data before the analysis. Human clustering of mass data cannot meet the requirement of data mining, therefore, various auto clustering algorithms come out successively. Spectral clustering is a commonly-used cluster algorithm and the effect of spectral clustering highly depends on similarity matrix. Gaussian kernel method has the problem with selecting the good parameter. In real world data set, there is always noise. It is hard to select a good parameter to construct an ideal similarity matrix by Gaussian kernel function. Method: This paper proposes a similarity matrix constructing method based on locally linear embedding. This kind of graph is sparser than Gaussian method and has little noise. This method is not sensitive to noise compared with Gaussian kernel function. The experiments on real world data sets prove the effect of this method. Result: This paper starts from the locally linear expression relationship, uses the non-negative linear value constructing similarity matrix and gets a better experiment result.
With the development of information technology and network, the face of today's large amount of data, data mining needs to be carried out in order to get useful information. The analysis and processing of these hu...
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With the development of information technology and network, the face of today's large amount of data, data mining needs to be carried out in order to get useful information. The analysis and processing of these huge data requires computing powerful cloud computing. Based on the cloud computing technology, the vehicle trajectory was researched by data mining. Based on the basic principles of cloud computing and data mining, a vehicle trajectory data mining system based on cloud computing was constructed. The clustering algorithm was used to cluster the vehicle trajectory. Finally, the system was implemented and tested. The realization results show that the system can obtain the trajectory information of the vehicle effectively, which makes a certain contribution to the management of vehicles and the supervision of traffic.
clustering algorithm is one of the hotspots for studying routing protocol in wireless sensor networks, which can help to enlarge the network topology and utilize the capacity of channel efficiently. In this paper, we ...
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ISBN:
(纸本)9781424479351
clustering algorithm is one of the hotspots for studying routing protocol in wireless sensor networks, which can help to enlarge the network topology and utilize the capacity of channel efficiently. In this paper, we present a maximum degree and negotiation strategy based clustering algorithm to solve the cluster overlapping problem. It selects candidate according to maximum degree and determines the Cluster-Head according to the negotiation strategy. The negotiation strategy can also be applied to the weighted clustering algorithm. The simulation results demonstrate that the proposed algorithms can remove the cluster overlapping phenomenon of the original algorithms and prolong the lifetime of the wireless sensor networks.
A hybrid clustering algorithm based on the artificial immune theory is presented in this paper. The method is inspired by the clone selection and memory principle. The problem of local optimal can be avoided by introd...
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ISBN:
(纸本)9783642247279
A hybrid clustering algorithm based on the artificial immune theory is presented in this paper. The method is inspired by the clone selection and memory principle. The problem of local optimal can be avoided by introducing the differentiation of memory antibody and inhibition mechanism. In addition, the K-means algorithm is used as a search operator in order to improve the convergence speed. The proposed algorithm can obtain the better data convergence compared with the K-means algorithm based clustering approach and artificial immune based approach. Simulate experimental results indicate the hybrid algorithm has a faster convergence speed and the obtained clustering centers can get strong stability.
In this paper, a novel data clustering algorithm based on the subtractive clustering (SC) algorithm and a new validity index are proposed. The SC algorithm is a simple method for data clustering;however, it has two pr...
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ISBN:
(纸本)9781424473175
In this paper, a novel data clustering algorithm based on the subtractive clustering (SC) algorithm and a new validity index are proposed. The SC algorithm is a simple method for data clustering;however, it has two problems which must be overcome. The first problem is such that the cluster centers found by SC are taken from data with the highest potential values, but that this data may not be the real cluster centers. The second problem is such that the cluster number generated by the SC algorithm is influenced by a predefined parameter. The proposed algorithm is based on distance relations between data and centers and is designed to ascertain the real centers generated by the SC algorithm. In addition, a novel robust cluster index is proposed to identify the real cluster number generated by SC algorithm.
The key of data mining algorithm in cloud computing environment is association rules and cluster analysis. The Apriori algorithm and K-means algorithm which are widely used nowadays have many problems such as long sca...
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The key of data mining algorithm in cloud computing environment is association rules and cluster analysis. The Apriori algorithm and K-means algorithm which are widely used nowadays have many problems such as long scanning time and large memory consumption. Therefore, a parallelized design scheme was proposed in this paper, which improved Apriori algorithm and K-means algorithm and was practiced by utilizing Hadoop platform, the feasibility of parallel project in massive data processing was discussed. The results showed that this proposed method could reduce the computational load of single node, reduce the computing time and improve the efficiency of the algorithm by repeating the calculation work on each node.
clustering algorithms are often adopted in batch process monitoring,especially in multistage batch process *** process monitoring,the process data are sampled at a specified time interval and generated as time *** thi...
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ISBN:
(纸本)9781509009107
clustering algorithms are often adopted in batch process monitoring,especially in multistage batch process *** process monitoring,the process data are sampled at a specified time interval and generated as time *** this paper,we propose a novel order clustering based sub-stage division(OCSD) algorithm which utilizes the order information(time sequence information) of batch operation and avoids the problem of out of *** is different from the conventional clustering algorithms,such as k-means and Fuzzy C-means(FCM),which assume the processing data are independent and identically distributed(i.i.d.) and do not consider the time sequence *** applied the proposed algorithm to a typical batch process,penicillin fermentation process and found that monitoring based OCSD can largely reduce the rate of missing alarm and false alarm compared with conventional clustering algorithms.
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