clustering plays a major role in various disciplines including biological visual image classification. Various clusteringalgorithms have been introduced and developed. Among them, the density-basedclustering algorit...
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clustering plays a major role in various disciplines including biological visual image classification. Various clusteringalgorithms have been introduced and developed. Among them, the density-based clustering algorithm achieves the best performance. The cellular neural network (CNN) basedclusteringalgorithm (CNNCA) is a density-based method. It performs the clustering at a very high speed because the CNN can be implemented on a silicon chip. Nevertheless, the performance of the CNNCA is significantly degraded when facing clusters of complex structures, especially with significant density differences between clusters. In this paper, the local rules of the CNNs are modified. Moreover, the multi-spatial resolutions are employed. In particular, the values of minimum spatial resolution and the other parameters are adaptively designed. Hence, the iterative procedures can be applied to the remaining unassigned points. Furthermore, the conditional minimum distance rule is employed to assign the dispersed elements to the existing clusters. Different datasets have been utilized to evaluate the performance of our proposed method. Compared to the state-of-the-art clusteringalgorithms, the computer numerical simulation results show that our proposed method outperforms the existing state-of-the-art methods in higher accuracy and more robustness.
The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable divisio...
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The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clusteringalgorithmbased on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of data points, we propose an adaptive division strategy (ADG) to divide the grid adaptively. In the second stage, we design a weighted grid construction strategy (NE) which can strengthen the relevance between grids to improve the accuracy of clustering. Meanwhile, based on the weighted grid and information entropy, we design a density calculation strategy (WGIE) to calculate the density of the grid. And last, to improve the parallel efficiency, core clusters computing algorithmbased on MapReduce (COMCORE-MR) are proposed to parallel compute the core clusters of the clusteringalgorithm. In the third stage, based on disjoint-set, we propose a core cluster merging algorithm (MECORE) to speed-up ratio the convergence of merged local clusters. Furthermore, based on MapReduce, a core clusters parallel merging algorithm (MECORE-MR) is proposed to get the clusteringalgorithm results faster, which improves the core clusters merging efficiency of the density-based clustering algorithm. We conduct the experiments on four synthetic clusters. Compared with H-DBSCAN, DBSCAN-MR and MR-VDBSCAN, the experimental results show that the DBWGIE-MR algorithm has higher stability and accuracy, and it takes less time in parallel clustering.
clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data griddi...
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clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density- basedclusteringalgorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clusteringalgorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clusteringalgorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.
The capacitated vehicle routing problem (CVRP) is one of the most challenging problems in the optimization of distribution. Most approaches can solve case studies involving less than 100 nodes to optimality, but time-...
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ISBN:
(纸本)9781538639818
The capacitated vehicle routing problem (CVRP) is one of the most challenging problems in the optimization of distribution. Most approaches can solve case studies involving less than 100 nodes to optimality, but time-consuming. To overcome the limitation, this paper presents a novel two-phase heuristic approach for the capacitated vehicle routing problem. Phase I aims to identifying sets of cost-effective feasible clusters through an improved density-based clustering algorithm. Phase II assigns clusters to vehicles and sequences them on each tour. Max-min ant system is used to order nodes within clusters. The simulation results indicate efficiency of the proposed algorithm.
Objective: Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depressi...
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Objective: Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Methods and material: Here, a new densitybasedclusteringalgorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clusteringalgorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. Results: The performance of NDEC and other employed clusteringalgorithms were evaluated using dice ratio as an external evaluation criteria and densitybasedclustering validation (DBCV) index as an internal evaluation metric. Across all employed clusteringalgorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). Conclusions: NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering nat
Indoor localization applications are expected to become increasingly popular on smart phones. Meanwhile, the development of such applications on smart phones has brought in a new set of potential issues (e.g., high ti...
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ISBN:
(纸本)9781479922864
Indoor localization applications are expected to become increasingly popular on smart phones. Meanwhile, the development of such applications on smart phones has brought in a new set of potential issues (e.g., high time complexity) while processing large datasets. The study in this paper provides an enhanced density-based cluster learning algorithm for the autonomous indoor localization algorithm DCCLA (density-basedclustering Combined Localization algorithm). In the enhanced algorithm, the density-basedclustering process is optimized by "skipping unnecessary density checks" and "grouping similar points". We conducted a theoretical analysis of the time complexity of the original and enhanced algorithm. More specifically, the run times of the original algorithm and the enhanced algorithm are compared on a PC (personal computer) and a smart phone, identifying the more efficient density-based clustering algorithm that allows the system to enable autonomous Wi-Fi fingerprint learning from large Wi-Fi datasets. The results show significant improvements of run time on both a PC and a smart phone.
Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these dema...
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Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers' refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user's nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user's subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.
This paper presents online criteria of frequency stability-based controlled islanding scheme based on the density-based clustering algorithm and the system electrical distance from wide-area measuring system (WAMS) da...
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This paper presents online criteria of frequency stability-based controlled islanding scheme based on the density-based clustering algorithm and the system electrical distance from wide-area measuring system (WAMS) data. For this issue, at each time window, based on evaluating the correlation coefficient criteria between all two pairs of synchronous generators (SGs), the possible coherent generator groups are identified. Next, by using clusteringalgorithm, the identified coherent SGs are clustered and sorted through online working mode. In this case, an online index is proposed which and developed index in the form of mixed-integer linear programming (MILP) which through real-time evaluations, the system frequency stability is evaluated. In the use of MILP processes, the network electrical distance is considered as the main objective function (OF) which by solving the proposed OF through minimum criteria, proper islanding locations are identified. Finally, at the post-islanding period, a nonlinear programming (NLP) procedure is performed through each individual islands where corresponding proper operational feasibilities are satisfied. In this case of transient stability criteria, based on the coherent SGs located at each island, the stability criteria is preserved through coherent groups as the core of islands. Also, by allocating proper load buses based on the SGs governor capacities, the islands frequency stabilities are controlled through acceptable regions. The effectiveness of the proposed scheme is carried through two IEEE 39-bus test system and Iran practical power grid. Results present the effectiveness of the proposed approach through different islanding conditions. (c) 2021 Elsevier Ltd. All rights reserved.
with the strong impact of OTT (Over The Top) business in the mobile Internet era, operators urgently need to discover the user value information from massive data to help them provide personalized accurate services an...
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ISBN:
(纸本)9781728105482
with the strong impact of OTT (Over The Top) business in the mobile Internet era, operators urgently need to discover the user value information from massive data to help them provide personalized accurate services and expand business customer services. The construction of social user groups based on mobile communication data can help operators to accurately analyze customer social structures, thus promoting quality service and improving marketing quality. In this paper, we design a set of social group construction algorithmbased on user behavior characteristics excavated from massive user data in mobile communication network. Due to the huge volume of mobile communication data sets, a parallel design based on MapReduce is exploited. The experimental results show that the ADBLINKw algorithm performs well on the efficiency and community detection quality.
The Phase-Resolved-Partial-Discharge pattern (PRPD) is a conventional technique used for the evaluation of partial discharges (PD) phenomena in High-VoltageAlternating-Current (HVAC) systems. This map is constructed b...
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ISBN:
(纸本)9781665467957
The Phase-Resolved-Partial-Discharge pattern (PRPD) is a conventional technique used for the evaluation of partial discharges (PD) phenomena in High-VoltageAlternating-Current (HVAC) systems. This map is constructed by plotting the peak of each detected pulses as a function of the phase angle of the supply voltage. Therefore it is obvious that this technique cannot be used for the analysis of data from PD mesaurement under different supply voltage condition (DC). The aim of this paper is to evaluate the application of the Time-Frequency map (TF map) for the analysis of a dataset obtained from PD measurement under DC voltage. A density-based clustering algorithm was also used to gain more insight from the collected data. The results show that, with this approach, it's possible to perform a noise rejection and identify PD pulses.
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