the analysis method of mathematical modeling in particular clustering process of mixed data is of great significance for improving the ability of data analysis. The traditional method for specific clustering process m...
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ISBN:
(纸本)9781510803084
the analysis method of mathematical modeling in particular clustering process of mixed data is of great significance for improving the ability of data analysis. The traditional method for specific clustering process mathematics modeling of mixed data is based on K-Means clustering algorithm, it is easy to fall into local convergence, and clustering effect is poor. Therefore, the analysis method of mathematical modeling in particular clustering process of mixed data is proposed in the paper based on particle swarm density maximum distance concave function and boundary membership degree feature analysis. The mixed data clustering sample points are divided into k classes according to the degree of similarity to cluster centers, dimensionality reduction is performed for differentiation characteristics of primitive variable data, through searching particles in the space, each particle has the speed, position and fitness, and the optimal solution is found by iteration, preprocessing for data standardization is conducted, data preprocessing includes scale selection of number, type and characteristics, boundary membership feature analysis is processed to achieve mathematical modeling analysis for specific clustering process of mixed data. The simulation results show that, the algorithm has the superior clustering performance of mixed data, good convergence, and great application value.
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC...
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For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.
In this paper, we point out that the counterexample constructed by Yu et *** incorrect by using scientific computing software *** means that the example cannot negate the convergence theorem of maximum entropy cluster...
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In this paper, we point out that the counterexample constructed by Yu et *** incorrect by using scientific computing software *** means that the example cannot negate the convergence theorem of maximum entropy clustering ***, we construct an example to negate Theorem 1 in Yu’s paper, and we propose Proposition 3 to prove that the limit of the iterative sequence is a local minimum of the objective function while v varies and u remains ***, we give a theoretical proof of the convergence theorem of maximum entropy clustering algorithm.
In order to conserve energy in wireless sensor networks (WSNs), sensor nodes are partitioned into clusters. clustering algorithm provides an effective way to extend the network lifetime of WSNs. The operation of clust...
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ISBN:
(纸本)9781509004782
In order to conserve energy in wireless sensor networks (WSNs), sensor nodes are partitioned into clusters. clustering algorithm provides an effective way to extend the network lifetime of WSNs. The operation of clustering algorithm is divided into cluster heads (CHs) selection phase and cluster formation phase. However, most of the previous researches have focused on CHs selection, and have not considered the cluster formation phase, which is important problem in WSNs and can drastically affect the network lifetime in WSNs. In this paper, cluster formation using fuzzy logic (CFFL) approach has been proposed to prolong network lifetime and reduce energy consumption in WSNs. this approach uses fuzzy logic in the formation cluster phase, two fuzzy parameters are used. These parameters are residual energy which is energy level of each CH and closeness to base station (BS) which is the distance between the CH and the BS. Simulation results show that the proposed approach consumes less energy and prolongs the network lifetime compared with Low Energy Adaptive clustering Hierarchy (LEACH) protocol.
As more and more mobile applications are developed, mobile app testing and quality assurance have become very important. Due to the diversity of mobile devices and platforms, compatibility testing for mobile apps has ...
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ISBN:
(纸本)9781479983568
As more and more mobile applications are developed, mobile app testing and quality assurance have become very important. Due to the diversity of mobile devices and platforms, compatibility testing for mobile apps has been identified as one urgent and challenging issue. There are two major reasons contributing to this issue. They are: a) the large number of mobile devices with diverse features and platforms which are upgraded frequently;b) a higher cost and complexity in mobile app compatibility testing. This paper proposes one optimized compatibility testing strategy using a statistical approach to reduce test costs, and improve engineer's operation efficiency. The paper provides a solution to generate an optimized compatibility test sequence for mobile apps using the K-Means statistical algorithm. A compatibility testing service has been proposed for mobile apps. Moreover, two case study results are reported to demonstrate its potential application and effectiveness.
Wireless sensor networks are those that are used for the communication between various sensor nodes to the base station. Heterogeneity in such networks is used to manage the network deployment cost and the network tra...
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ISBN:
(纸本)9781479964383
Wireless sensor networks are those that are used for the communication between various sensor nodes to the base station. Heterogeneity in such networks is used to manage the network deployment cost and the network traffic. A hierarchical cost effective LEACH (HCEL) protocol is proposed to enhance energy efficiency of the sensor nodes thereby maximizing the network performance without increasing the network deployment cost. In this paper, heterogeneous networks that comprise three types of sensor nodes are considered. A hierarchical network structure is formed where the data are forwarded by using "aggregators". The clustering is done inorder to maximize the energy efficiency of the sensor nodes. The cost comparision is done between various protocols like separate LEACH(SL), proposedLEACH(PL), separate proposed(SP) and HCEL. The energy efficiency is derived by initiating the activity window interference. Simulation results show that the HCEL protocol derive a gradual decrease in the network deployment cost ratio in terms of powerful nodes and energy factor.
A novel multi-targets ISAR imaging method based on particle swarm optimization (PSO) and modified CLEAN technique is proposed in this paper. First, multi-targets are modeled as several separated group-targets in which...
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ISBN:
(纸本)9781467372978
A novel multi-targets ISAR imaging method based on particle swarm optimization (PSO) and modified CLEAN technique is proposed in this paper. First, multi-targets are modeled as several separated group-targets in which translational motion of each target is analogous. And then, translational motion of each group-target is modeled as a polynomial, and the polynomial coefficient is estimated via PSO. Then focused image of the group-target can be obtained and extracted via a modified CLEAN technique. Meanwhile, each target can be segmented and extracted based on clustering number estimation and K-means clustering algorithm. Finally, better focused image of each target would be obtained through further traditional mono-target imaging processing. Experimental results verify the validity of the proposed method.
The Chameleon algorithm plays an important role in data mining and data analysis. Membrane computing, as a new kind of parallel biological computing model, can reduce the time complexity and improve the computational ...
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ISBN:
(纸本)9783662490143;9783662490136
The Chameleon algorithm plays an important role in data mining and data analysis. Membrane computing, as a new kind of parallel biological computing model, can reduce the time complexity and improve the computational efficiency. In this study, an agglomerate Chameleon algorithm is proposed which generates the sub-clusters by the K-medoids algorithm method. Then, the agglomerate Chameleon algorithm based on the Tissue-like P system is constructed with all the rules being created. The time complexity of the proposed algorithm is decreased from O(K * (n - K)(2) * C-n(K)) to O(n * C-n(K)) through the parallelism of the P system. Experimental results show that the proposed algorithm has low error rate and is appropriate for big cluster analysis. The proposed algorithm in this study is a new attempt in applications of membrane system and it provides a novel perspective of cluster analysis.
Recent advances in proteomic technologies have enabled high-throughput binary data on protein-protein interactions of E. coli to be released into public domain, and many protein complexes have been identified by exper...
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ISBN:
(纸本)9781467367981
Recent advances in proteomic technologies have enabled high-throughput binary data on protein-protein interactions of E. coli to be released into public domain, and many protein complexes have been identified by experimental methods. Although it has a long study history, a large-scale analysis of protein complex in binary PPI network of E. coli is still absent. We used a novel link clustering algorithm named ELPA to infer protein complexes and functional modules in E. coli PPI network. By mapping our results to 276 gold standard protein complexes and protein function annotations offered by EcoCyc, we found that 80.2% of predicted modules mapping well with one or more complexes, while 92.8% of predicted modules tally well with certain GO terms. Furthermore, we compare our results with MCL algorithm, and evaluated our results with several accuracy measures and biological relevance, the result shows that ELPA achieved an average 18.3% improvement over MCL based on the accuracy measures, which means our method will contributes to uncover the complexes of Ecoli.
Efficiency of broadcast transmission based on Cluster Heads in clusterized wireless network has been assessed in this paper. The proposed algorithm uses MPR (Multi Point Relay) method based on Cluster Heads (CH-MPR-BC...
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ISBN:
(纸本)9788393484850
Efficiency of broadcast transmission based on Cluster Heads in clusterized wireless network has been assessed in this paper. The proposed algorithm uses MPR (Multi Point Relay) method based on Cluster Heads (CH-MPR-BC). Most results were compared with classical solution which uses GateWay nodes at the edge of clusters as retransmitters. It is shown that for clustered network, CH-MPR-BC can be reliable and effective energetically. Tests have been done for static network assuming Free Space Path Loss propagation model with assumed reliability of information delivery in radio link. Energy results were additionally compared with Classical Flooding and MPR algorithms.
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