For most small and medium-sized enterprise (SME), the establishment of special customer management system requires specialized hardware and professional technicians, which will greatly increase the management cost of ...
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For most small and medium-sized enterprise (SME), the establishment of special customer management system requires specialized hardware and professional technicians, which will greatly increase the management cost of enterprises. Customer relationship management (CRM) based on cloud computing can not only reduce the management cost of SME, but also improve the business agility of SME, and help SME create new business models and market opportunities. In this study, a CRM system based on customer service is designed by using the customer classification method based on the improved K-means clustering algorithm. The system adopts the system architecture model based on network to realize the information interaction and resource sharing among different roles. The management of customer service is the core content of system design and development, and it realizes the development of CRM system for SME. The whole system effectively solves the problems such as low efficiency and limited service provision of customer management, as well as the low integrity of customer work, so as to achieve the purpose of improving the management capacity of SME.
In the Industrial Internet of Things (IIoT), wireless sensor network (WSN) technology makes devices that communicate with each other. The information integrated from multiple data sources will be transformed into prod...
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In the Industrial Internet of Things (IIoT), wireless sensor network (WSN) technology makes devices that communicate with each other. The information integrated from multiple data sources will be transformed into productivity. However, the clusters close to the base station take a considerable load over multi-hop transmission, and in this case, the lifetime of the industrial WSN is restricted. To solve this problem, a grid-based clustering algorithm via load analysis for IIoT is presented in this paper. First, the network load is quantitatively analyzed and then a load model is constructed. Furthermore, a set of expressions is deduced to indicate the network load distribution. It is concluded that the number of delivered packets in each level is related to the grid length at that level. The optimal grid length is obtained by solving polynomials to achieve the uniform energy consumption of nodes at each level. Finally, the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster. Results of the experiments show that compared with ACT, ER-HEED, and RUHEED, our algorithm balances energy depletion effectively and extends the whole network lifetime.
Many contemporary data sources in a variety of domains can naturally be represented as fully-dynamic streaming graphs. How to design an efficient online streaming clustering algorithm on such graphs is of great concer...
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Many contemporary data sources in a variety of domains can naturally be represented as fully-dynamic streaming graphs. How to design an efficient online streaming clustering algorithm on such graphs is of great concern. However, existing clustering approaches are inappropriate for this specific task because: (1) static clustering approaches require expensive computational cost to cluster the graph for each update and (2) the existing streaming clustering neither could fully support insertion/deletion of edges nor take temporal information into account. To tackle these issues, in this work, firstly we propose an appropriate streaming clustering model and design two new core components: streaming reservoir and cluster manager. Then we present an evolution-aware bounded-size clustering algorithm to handle the edge additions/deletions. It requires the clusters to satisfy the maximum cluster-size constraint, and maintains the recency of edges in the temporal sequence and gives high priority to the recent edges in each cluster. The experimental results show that the proposed BSC algorithm outperforms current online algorithms and is capable to keep track of the evolution of graphs. Furthermore, it obtains almost one order of magnitude higher throughput than the state-of-the-art algorithms.
Neutrosophy (neutrosophic logic) is used to represent uncertain, indeterminate, and inconsistent information available in the real world. This article proposes a method to provide more sensitivity and precision to ind...
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Neutrosophy (neutrosophic logic) is used to represent uncertain, indeterminate, and inconsistent information available in the real world. This article proposes a method to provide more sensitivity and precision to indeterminacy, by classifying the indeterminate concept/value into two based on membership: one as indeterminacy leaning towards truth membership and the other as indeterminacy leaning towards false membership. This paper introduces a modified form of a neutrosophic set, called Double-Valued Neutrosophic Set (DVNS), which has these two distinct indeterminate values. Its related properties and axioms are defined and illustrated in this paper. An important role is played by clustering in several fields of research in the form of data mining, pattern recognition, and machine learning. DVNS is better equipped at dealing with indeterminate and inconsistent information, with more accuracy, than the Single-Valued Neutrosophic Set, which fuzzy sets and intuitionistic fuzzy sets are incapable of. A generalised distance measure between DVNSs and the related distance matrix is defined, based on which a clustering algorithm is constructed. This article proposes a Double-Valued Neutrosophic Minimum Spanning Tree (DVN-MST) clustering algorithm, to cluster the data represented by double-valued neutrosophic information. Illustrative examples are given to demonstrate the applications and effectiveness of this clustering algorithm. A comparative study of the DVN-MST clustering algorithm with other clustering algorithms like Single-Valued Neutrosophic Minimum Spanning Tree, Intuitionistic Fuzzy Minimum Spanning Tree, and Fuzzy Minimum Spanning Tree is carried out.
clustering routing algorithm is an important part of network layer in Ad Hoc *** main goal is to find an optimal multicast tree which can satisfy QoS constraints,the network can not only meet the QoS requirements of d...
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clustering routing algorithm is an important part of network layer in Ad Hoc *** main goal is to find an optimal multicast tree which can satisfy QoS constraints,the network can not only meet the QoS requirements of data services,but also improve the efficiency of limited network ***(Weight-based clustering Routing algorithm),a clustering routing algorithm,is designed in this *** adopts the combination weighting method,and the cluster topology is *** the clustering strategy is changed properly,the correlated cluster or uncorrelated cluster can be constructed *** can also adjust itself according to the actual application,improve the fairness of node competition and optimize the cluster *** show that it can significantly improve the transmission rate of mobile network nodes,enhance the stability of the network and provide an application basis for QoS services in Ad Hoc networks.
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.
The paper presented here entails technique that is based upon Sorensen's similarity coefficient as well as the Sorensen- Single Linkage clustering (SLC) algorithm, which essentially is a hybrid clustering approach...
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The paper presented here entails technique that is based upon Sorensen's similarity coefficient as well as the Sorensen- Single Linkage clustering (SLC) algorithm, which essentially is a hybrid clustering approach that has been deployed here to show issue related to cell formation during the process of cellular engineering. In the following paper and study that has been presented here, the suggested hybrid algorithm comparison has been drawn versus the existing clustering algorithms and their performances have been compared specifically the MOD-SLC modified single linkage or SLC also known as the single linkage clustering algorithm. The Sorensen-SLC technique and the inferences drawn on the same show that it performs comparatively better when compared with the MOD-SLC and SLC algorithms. Additionally, the computation that is necessary for this hybrid algorithm is absolutely a bare minimal and the computation processing is effective and easy.
We adopt clustering algorithm to improve segmentation accuracy. In this paper, 3D laser scanning platform was built to obtain the spatial 3D point cloud data. And then we extracted the point cloud data for two planar ...
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ISBN:
(纸本)9781538660058
We adopt clustering algorithm to improve segmentation accuracy. In this paper, 3D laser scanning platform was built to obtain the spatial 3D point cloud data. And then we extracted the point cloud data for two planar features. K-means algorithm, density-based clustering algorithm and density peak clustering algorithm were employed to split the 3D point cloud of the two planes. After clustering, we compared and analyzed the clustering results of the three clustering algorithms. More importantly, we also found that for peak density clustering, the threshold value is related to its sensitivity to noise points. After fitting the two planes, the verticality of two planes was also calculated. We analyzed the results and summarized the criterion for selecting thresholds.
Energy efficient clustering protocols are deeply studied for low power, multi-functional wireless sensors networks (WSNs), clustering have to ensure connectivity and reliability in WSN even in large scale environment....
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ISBN:
(纸本)9781538653050
Energy efficient clustering protocols are deeply studied for low power, multi-functional wireless sensors networks (WSNs), clustering have to ensure connectivity and reliability in WSN even in large scale environment. In this paper, we present a new clustering approach called the Fixed Competition-based clustering Approach (FCBA) based routing algorithm to fairly use the energy of the sensors to maximize the network lifetime. The Selecting of cluster heads with FCBA is performed based upon a residual energy and the distances among the cluster heads. The simulation results are given to validate the analytical results. The experimental results indicate that proposed protocol leads to reduction of sensor's nodes energy consumption and prolongs the network lifetime, significantly.
In the era of the explosion of data volume, data processing is often performed in the traditional way for such orders of magnitude. The emergence of Hadoop big data platform has led people to think of combining big da...
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In the era of the explosion of data volume, data processing is often performed in the traditional way for such orders of magnitude. The emergence of Hadoop big data platform has led people to think of combining big data platform with clustering algorithm to improve data processing efficiency;This paper studies the k-means algorithm based on the big data platform, studies the acceleration ratio and iteration frequency of k-means, first builds the Hadoop big data platform, and verifies the acceleration ratio of k-means in cluster and pseudo-distributed environment through multiple experiments. Through the comprehensive analysis of the recording time and the number of iterations, it is finally verified that the combination of k-means algorithm and canopy algorithm can improve the clustering accuracy and efficiency more effectively.
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