For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this proble...
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For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this problem, such as introducing more representative points per cluster, considering both interconnectivity and closeness, and adopting the density-based method. However, the density defined in DBSCAN is decided by minPts and Eps, and it is not the best solution to describe the data distribution of one cluster. In this paper, a deviation factor model is proposed to describe the data distribution and a novel clustering algorithm based on artificial immune system is presented. The experimental results show that the proposed algorithm is more effective than DBSCAN, k-means, etc.
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering alg...
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The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering algorithm was introduced, and then it was improved based on the node similarity to get better partition results. Finally, experiments were carried out on the random network and the real network. The results showed that the improved clustering algorithm was faster than GN and KL algorithms, the community had larger modularity, and the purity was closer to 1. The experimental results show the effectiveness of the proposed method and make some contributions to the reliable community division.
clustering is a widely used technique of finding interesting patterns residing in the dataset that were not obviously known. It is a division of data into groups of similar objects. The clustering of large data sets h...
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ISBN:
(纸本)0780390504
clustering is a widely used technique of finding interesting patterns residing in the dataset that were not obviously known. It is a division of data into groups of similar objects. The clustering of large data sets has received a lot of attention in recent years, however, clustering is still a challenging task since many cluster algorithms fail to do well in scaling with the size of the data set and the number of dimensions that describe the points, or in finding arbitrary shapes of clusters., or dealing effectively with the presence of noise. This paper describes a clustering method for unsupervised classification of objects in large data sets. The new methodology combines the simulating annealing algorithm with CLARANS (clustering Large Application based upon Randomized Search) in order to cluster large data sets efficiently. At last. the method is experimented on the generated data set. The result shows that the approach is quick than CLARANS and can produce a similar division of data as CLARANS.
In this paper, we surveyed the development of maximum-entropy clustering algorithm, pointed out that the maximum-entropy clustering algorithm is not new in essence, and constructed two examples to show that the iterat...
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In this paper, we surveyed the development of maximum-entropy clustering algorithm, pointed out that the maximum-entropy clustering algorithm is not new in essence, and constructed two examples to show that the iterative sequence given by the maximum-entropy clustering algorithm may not converge to a local minimum of its objective function, but a saddle point. Based on these results, our paper shows that the convergence theorem of maximum-entropy clustering algorithm put forward by Kenneth Rose et al. does not hold in general cases.
Wireless sensor networks commonly consist of a large number of tiny sensor nodes that are deployed either inside the target area or very close to it to cooperatively monitor the target area. Energy efficiency and netw...
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Wireless sensor networks commonly consist of a large number of tiny sensor nodes that are deployed either inside the target area or very close to it to cooperatively monitor the target area. Energy efficiency and network lifetime are two challenges that most of researchers deal with. In this paper, to improve the performance of sensor networks, we propose an energy efficient competitive clustering algorithm for wireless sensor networks using a controlled mobile sink. clustering algorithm can effectively organize sensor nodes and the use of a controlled mobile sink node can mitigate hot spot problem or energy holes. The selection of optimal moving trajectory for sink nodes is an NP-hard problem. In our algorithm, we firstly study an competitive clustering algorithm in which cluster heads are rotated in each round and selected mainly based on their competition range and their residual energy. Besides, we use mobile sink node instead of fixed sink node. The mobile sink node moves at a certain speed along a predefined path and sojourn at some park position to collect data packets. Simulation results validate that competitive clustering algorithm outperforms LEACH and the use of mobile sink node significantly improve the performance of the sensor network.
From the point of view of modern educational technology development, the development process CAI from single computer to network is introduced. Through the induction of learning theory and education theory in differen...
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ISBN:
(纸本)9780769539362
From the point of view of modern educational technology development, the development process CAI from single computer to network is introduced. Through the induction of learning theory and education theory in different stages, the modern CAI and its main development mode are analyzed. Combining with computer software technology and network technology development, the system design of an open CAI platform is put forward. Using the network platform, the basic concepts and theories requirements of modern education theory and learning theory are realized. Using clustering algorithm and rough set theory, the application of network learning theory and design principle in CAI is studied. The results show that, with the continuous development of network technology, application of network learning theory and design principle in the fields of CAI will play an increasingly important role. In conclusion, in the near future, a truly mature and open learning environment will be able to build to achieve the greatest range of educational resources sharing, which makes the popularity of CAI into reality.
作者：
Zhang YihuaJimei Univ
Coll Business Adm Dept Informat Management Xiamen 361021 Fujian Peoples R China
In possession of great customer-data, Mobile Enterprise must transform data advantage into competitive advantage, which is not only to maximize income, but also to enhance the management system of channel. In this pap...
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ISBN:
(纸本)9781424435197
In possession of great customer-data, Mobile Enterprise must transform data advantage into competitive advantage, which is not only to maximize income, but also to enhance the management system of channel. In this paper, I applied the theory of cluster analysis to discuss the use of the cluster analysis technology in the mobile market segmentation field, and by integrating the basic-data I utilized the mobile improvement K-means to set up a channel subsection model in rural areas in order to divide the rural mobile market effectively.
Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral...
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Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral images are based on a per-pixel classification, which uses only spectral information and ignores spatial information. A clustering algorithm based on both spectral and spatial information would produce better results. In this work, spatial refinement clustering (SpaRef), a new clustering algorithm for multispectral images is presented. Spatial information is integrated with partitional and agglomeration clustering processes. The number of clusters is automatically identified. SpaRef is compared with a set of well-known clustering methods on compact airborne spectrographic imager (CASI) over an area in the Klompenwaard, The Netherlands. The clusters obtained show improved results. Applying SpaRef to multispectral chemical images would be a straight-forward step. (C) 2003 Elsevier B.V. All rights reserved.
Link flooding attack (LFA) is considered as a new class of DDoS attacks. Traditional DDoS attacks against end nodes, while LFA focuses on target links in the network. LFA is difficult to detect because of large-scale ...
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ISBN:
(纸本)9781665478366
Link flooding attack (LFA) is considered as a new class of DDoS attacks. Traditional DDoS attacks against end nodes, while LFA focuses on target links in the network. LFA is difficult to detect because of large-scale legitimate low-speed traffic flow. In this paper, we propose an improved weighted Euclidean distance Mean Shift algorithm named TD-MS(Time-Delay Mean Shift) and the decision feature P-TI of the LFA detection based on Poisson process for TD-MS. The Weighted coefficient and the decision feature are obtained from the delay rate and the probability of the incremental LFA packet time, respectively. We constructed the software-defined-network(SDN) experimental environment to collect LFA packets through forwarding decisions made by the control plane on the data plane. We first cluster the network traffic flow based on the delay feature of network traffic flow while LFA happening, and then on which verify the existence of LFA according to the decision feature generated from such clustering results. The experimental results show that, the TD-MS algorithm outperforms the existing machine learning methods such as Decision Tree (DT) and Bernoulli Naive Bayesian classifier (BernoulliNB)in the light of both accuracy and efficiency.
Smoothing b-splines constitute a powerful and popular methodology for performing nonparametric regression with high accuracy. It is well known that the placement of the knots in spline smoothing approximation has an i...
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ISBN:
(纸本)9781479900206
Smoothing b-splines constitute a powerful and popular methodology for performing nonparametric regression with high accuracy. It is well known that the placement of the knots in spline smoothing approximation has an important and considerable effect on the behavior of the final approximation. For this purpose, in this paper a novel methodology is presented for optimal placement and selections of knots, in order to approximate or fit curves to data, using smoothing splines. A new method based on improved clustering algorithm is used to optimally select a reduced number of knots for constructing the base of the b-spline, while ensuring the best accuracy.
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