The cultural heritage is a unique and irreplaceable witness of our past and it is vulnerable to natural disasters and anthropic behaviors. A rating evaluation system for smart museum environment parameter can serve as...
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ISBN:
(纸本)9781728158556
The cultural heritage is a unique and irreplaceable witness of our past and it is vulnerable to natural disasters and anthropic behaviors. A rating evaluation system for smart museum environment parameter can serve as a reference for decision-making. However, this field is in short in preventive conservation strategies aimed to assure the protection, or increase the life expectancy in museums. This paper proposes a rating evaluation for museum environment parameter data based on improved K-Means clustering algorithm by introducing weighted cluster center function. Presented improved K-Means improves the clustering effect by weakening the random initial cluster center selection easy to fall into the local optimal results. Experimental results demonstrate that the improved clustering algorithm is not only more stable in clustering process, but can reduce the impact of the noise data. The presented rating evaluation for museum environment parameter data can provide the basis for reasonable, effective regulation and control of museum environmental parameters.
In order to prolong the network lifetime, a novel energy-efficient clustering algorithm is proposed to adapt the characteristic of heterogeneous wireless sensor networks. In the proved clustering protocol, taking comm...
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ISBN:
(纸本)9781538645024
In order to prolong the network lifetime, a novel energy-efficient clustering algorithm is proposed to adapt the characteristic of heterogeneous wireless sensor networks. In the proved clustering protocol, taking communication distance and energy consumption into account comprehensively, cluster-heads are elected more evenly in clustering stage by a value based on minimum energy consumption rather than the physical position center. Simulation results indicate that the proved algorithm has longer network lifetime and superior performance compared with the original algorithms in heterogeneous environments.
This paper presents a new intuitionistic fuzzy cmeans (IFCM) clustering algorithm by adapting a new method to calculate the hesitation degree of data point in cluster. From the definition of fuzzy entropy, if a cluste...
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ISBN:
(纸本)9781479925650
This paper presents a new intuitionistic fuzzy cmeans (IFCM) clustering algorithm by adapting a new method to calculate the hesitation degree of data point in cluster. From the definition of fuzzy entropy, if a clustering result of a data point has bigger fuzzy entropy, the clustering result should have more uncertainty. It means that we have insufficient information to deal with the clustering of a data point, so the hesitation degree of clustering result of the data point should be greater. Form this opinion, a mathematical model is applied to calculate the hesitation degree of clustering of data point based on fuzzy entropy is given. An IFCM clustering algorithms is present. Experiments are performed using two-dimensional synthetic data-sets referred from previous papers. Results have shown that proposed algorithm is not only effective for linear and nonlinear separation, but also able to describe more information comparing to fuzzy c-means clustering algorithm.
Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propo...
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ISBN:
(纸本)9784885522963
Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two well-known clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.
To improve query efficiency, most image retrieval systems utilize clustering algorithms to build indices on image databases. In this paper, we analysis Region-Based Image Retrieval (RBIR) system query results, hypothe...
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ISBN:
(纸本)9781424400997
To improve query efficiency, most image retrieval systems utilize clustering algorithms to build indices on image databases. In this paper, we analysis Region-Based Image Retrieval (RBIR) system query results, hypothesis test and Hubert's F statistic for cluster validation. Our experiment results suggest that images originated from different categories do form clusters in the feature space and thus can be separated. We then analysis the performance of clustering algorithms including K-Means, Fuzzy C-Mean, CA-clustering, Density Based Spatial clustering of Applications with Noise (DBSCAN) and the proposed modified DBSCAN algorithm with a second merging phase. Our experiment results suggest that the proposed algorithm has clustering performance among the best algorithms. And the proposed modified DBSCAN algorithm with a second merging phase can avoid some of the drawbacks (e.g., k random initial cluster centers) in the original K-Means algorithms.
Since Ad hoc network Is vulnerable to attack, it's imperative to carry out intrusion detection research on ad hoc network. In this paper, according to the requirement of cluster based intrusion detection system, w...
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ISBN:
(纸本)9781424421077
Since Ad hoc network Is vulnerable to attack, it's imperative to carry out intrusion detection research on ad hoc network. In this paper, according to the requirement of cluster based intrusion detection system, we propose a new algorithm of clustering for IDS. Analyse and simulation results show that our method can decrease the number of cluster head and extend network survival time.
With the development of abnormal behavior analysis technology, measuring the similarity of abnormal behavior has become a core part of abnormal behavior detection. However, there are general problems of central select...
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ISBN:
(纸本)9783030005634;9783030005627
With the development of abnormal behavior analysis technology, measuring the similarity of abnormal behavior has become a core part of abnormal behavior detection. However, there are general problems of central selection distortion and slow iterative convergence with existing clusteringbased analysis algorithms. Therefore, this paper proposes an improved clustering-based abnormal behavior analysis algorithm by using K-means. Firstly, an abnormal behavior set is constructed for each user from his or her behavioral data. A weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior sets are proposed by using all the behavior sets. Secondly, an improved algorithm is developed, in which we calculate the tightness of all data points and select the initial cluster centers from the data points with high density and low density to improve the clustering effect based on the K-means clustering algorithm. Finally, clustering result of the abnormal behavior is got with the input of the eigenvalues of the abnormal behavior set. The results show that, the proposed algorithm is superior to the traditional clustering algorithm in clustering performance, and can effectively enhance the clustering effect of abnormal behavior.
Density-peaks-clustering (DPC) algorithm plays an important role in clustering analysis with the advantages of easy realization and comprehensiveness whereas without the requirement of any iteration or optimization. H...
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ISBN:
(纸本)9781728195391
Density-peaks-clustering (DPC) algorithm plays an important role in clustering analysis with the advantages of easy realization and comprehensiveness whereas without the requirement of any iteration or optimization. However, the DPC accuracy depends on two user-specified parameters, and each of them can greatly affect clustering results. To solve this problem, we extend DPC to a general and hierarchical form. Without the need of any parameters, the proposed E-DPC algorithm can effectively cluster points in any dataset with various characteristics. The results of experiments show that the proposed algorithm is more accurate and general in comparison with two mostly used algorithms.
This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and ...
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ISBN:
(纸本)9780878492695
This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.
A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalo...
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ISBN:
(纸本)9781450363518
A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalous score which represents the outlierness of a data point based on the largest radius of a ball containing only that data point. The new clustering algorithm based on this score is proposed called the extreme anomalous score clustering algorithm (ESC). It searches for a cluster representative by combining two data points which are placed with the smallest extreme anomalous score. Then all extreme anomalous scores are updated and the algorithm stops when it reaches the number of clusters defined by a user. Otherwise, it continues to combine two data points having the smallest extreme anomalous scores. The experimental results on three groups of simulated datasets report the superior performance of ESC over AGNES, k-means, and DBSCAN based on the silhouette measurement and the homogeneity measurement.
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