In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved Projection Pursuit based on Ant Colony Optimization algorithm ...
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ISBN:
(纸本)9783642163968
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved Projection Pursuit based on Ant Colony Optimization algorithm was introduced. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result. The study provides a novel algorithm for the high-dimension data clustering.
Data gathering in wireless sensor networks (WSNs) is one of the major sources for power consumption. Compression is often employed to reduce the number of packet transmissions required for data gathering. However, con...
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Data gathering in wireless sensor networks (WSNs) is one of the major sources for power consumption. Compression is often employed to reduce the number of packet transmissions required for data gathering. However, conventional data compression techniques can introduce heavy in-node computation, and thus, the use of compressive sensing (CS) for WSN data gathering has recently attracted growing attention. Among existing CS-based data gathering approaches, hierarchical compressive data gathering (HCDG) methods currently offer the most transmission-efficient architectures. When employing HCDG, clustering algorithms can affect the number of data transmissions. Most existing HCDG works use the random clustering (RC) method as a clustering algorithm, which can produce significant number of transmissions in some cases. In this paper, we present a compressibility-based clustering algorithm (CBCA) for HCDG. In CBCA, the network topology is first converted into a logical chain, similar to the idea proposed in PEGASIS [1], and then the spatial correlation of the cluster nodes' readings are employed for CS. We show that CBCA requires significantly less data transmission than the RC method with a little recovery accuracy loss. We also identify optimal parameters of CBCA via mathematical analysis and validate them by simulation. Finally, we used water level data collected from a real-world flood inundation monitoring system to drive our simulation experiments and showed the effectiveness of CBCA.
Color is an important element to consider when shaping urban ***,previous studies seldom included quantitative analyses of color relationships between urban agglomerations within proximal regions and with similar cult...
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Color is an important element to consider when shaping urban ***,previous studies seldom included quantitative analyses of color relationships between urban agglomerations within proximal regions and with similar cultures to distinguish and shape individual urban *** study focused on Xiamen,Zhangzhou,and Quanzhou metropolitan areas,which are influenced by Minnan culture,and collected natural and cultural landscape network images that collectively represent the urban landscape in *** extraction,computer vision processing technologies,and clustering algorithms,such as k-means partitioning,hierarchical methods,and co-occurrence frequency,were applied using image *** then established an urban color database and quantified color ***,we conducted a comparative analysis of dominant colors and color combination associations in Xiamen,Zhangzhou,and Quanzhou metropolitan areas to explore their similarities and differences and define their *** also considered other cities of the same type for comparison.
Social network analysis is a new research field in data mining. The clustering in social network analysis is different from traditional clustering. It requires grouping objects into classes based on their links as wel...
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Social network analysis is a new research field in data mining. The clustering in social network analysis is different from traditional clustering. It requires grouping objects into classes based on their links as well as their attributes. While traditional clustering algorithms group objects only based on objects' similarity, and it can't be applied to social network analysis. So on the basis of BSP (business system planning) clustering algorithm, a social network clustering analysis algorithm is proposed. The proposed algorithm, different from traditional BSP clustering algorithms, can group objects in a social network into different classes based on their links and identify relation among classes dynamically & require less amount of memory
the research to improve the performance of network problems, in order to improve the CDN network of user division to solve problems, and put forward a kind of fuzzy clustering algorithm to differentiate the user clust...
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ISBN:
(纸本)9780769549354
the research to improve the performance of network problems, in order to improve the CDN network of user division to solve problems, and put forward a kind of fuzzy clustering algorithm to differentiate the user cluster load balance algorithm, using this algorithm CDN user cluster, can detect the results from the client, update maintenance global load balance of content routing table, the user request redirect to more precise edge servers, improve service percentage. Make the content of the users need timely release, alleviate network congestion problem, enhance the user access to the network response speed.
Electrical equipment's family defects are common deficiencies usually caused by some particular factors such as the material of equipment and the process of design and manufacture. In order to classify these defec...
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ISBN:
(纸本)9781467390682
Electrical equipment's family defects are common deficiencies usually caused by some particular factors such as the material of equipment and the process of design and manufacture. In order to classify these defect data, the paper proposes a clustering algorithm combines PAM and FCM. The method uses PAM firstly to generate the cluster prototypes With the goal of lowering the initial randomness of FCM, and then it runs FCM to obtain the final clustering results. These steps are expected to ensure the accuracy of the algorithm and take less iteration. Experiments using electrical equipment' s family defects data-sets to test and verity the accuracy and efficiency of the algorithm are discussed. The results show that the combination methods used in this paper provide a better performance in both accuracy and run time when compared with traditional analysis approach like hierarchical clustering algorithm.
In this paper, a clustering algorithm based on the immune mechanism of the capture of antigen by the antibody has been presented. The datum to be clustered are viewed as antigens,and the cluster centers are viewed as ...
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ISBN:
(纸本)9780769536040
In this paper, a clustering algorithm based on the immune mechanism of the capture of antigen by the antibody has been presented. The datum to be clustered are viewed as antigens,and the cluster centers are viewed as the antibodies in the immune system. The clustering is effectively the process in which the immune system constantly generates antibodies for the recognition of the antigens and finally generates the optimal antibodies for the capture of the antigens. The experimental results show that the algorithm can successfully be applied to the data clustering.
Traditional Chinese medicine (TCM) is a holistic medical approach and the formula's composition discipline is still a mystery. Detecting a formula's structure and herb communities/clusters in TCM Formula netwo...
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ISBN:
(纸本)9781509001637
Traditional Chinese medicine (TCM) is a holistic medical approach and the formula's composition discipline is still a mystery. Detecting a formula's structure and herb communities/clusters in TCM Formula networks (TCMF) is a mainly existing problem in data mining of the data sets. In this paper, we devise a novel community similarity calculating method in the process of clustering, which is called Random Walk Hierarchical clustering (RWHC) algorithm, to identify herb communities by using clustering algorithms based on the formula network of atrophic lung disease. And we also use classic NG modularity function to evaluate the experimental results. The studies suggest that the TCM network clustering approach provides a new research paradigm for mining TCM data from an experience-based medicine, will accelerate TCM drug discovery, and also improve current drug discovery strategies.
Compared with text topic clustering, the granularity of news event clustering is finer. The complexity of the semantic relationship in news texts and their informational redundancy can cause great inconveniences for c...
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ISBN:
(纸本)9781538680346
Compared with text topic clustering, the granularity of news event clustering is finer. The complexity of the semantic relationship in news texts and their informational redundancy can cause great inconveniences for clustering. Therefore, the representation of events as well as the method for cluster division has become a major focus of researchers. To better tackle the problem, this paper proposes NEC_SRG, a news event clustering algorithm based on the semantic relationship graph. First, the semantic units related to the event topic are extracted from the news. Then, the semantic relationship graph is established based on the connection of words in each semantic unit to represent the event. After that, the cluster of semantic relationship graph is created based on the sub-graph closeness. Finally, the news events are clustered according to the distribution of the semantic units in the graph clustering results. Experiments show that the NEC_SRG algorithm has obvious advantages over similar algorithms.
This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, th...
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ISBN:
(纸本)9783319117492;9783319117485
This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e. g. hotspots discovery.
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