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With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots. [ABSTRACT FROM AUTHOR]

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems. [ABSTRACT FROM AUTHOR]

Summary: To overcome the shortcomings of K‐means clustering including clustering numbers, sensitivity to clustering center (seeds) and local optimization, this article proposes an improved genetic algorithm (GA) with a novel Lagrange‐based fitness function and an initial population technique(called NicheClust algorithm); the NicheClust can determine the best chromosomes and then feeds these into K‐means as initial seeds to achieve higher‐quality clustering results by allowing the initial seeds to readjust in terms of clustering demands. The GA approach is proposed to search for a global optimally solution. The initial population method is presented to automatically capture the appropriate number of clusters and find the initial seeds. The Lagrange‐based approach is used to prevent the fitness function from prematurely converging and capture global optimization for K‐means clustering results. Experimental results based on six taxi Global Positioning System (GPS) datasets verify the higher performance of NicheClust compared to other clustering methods and validate the effectiveness with statistical analysis method. [ABSTRACT FROM AUTHOR]

GPS location data is rapidly increasing and has become an important part of spatial information technology and its applications. In order to use K-means to discover hidden information behind GPS data, the drawbacks of K-means must be addressed, such as the difficulty of discovering the number of clusters, sensitivity to initial cluster center (seed) selection, and ease of falling into local optima. This paper presents a novel sharing-based niche genetic algorithm (NGA) with a novel initial population approach based on hybrid K-means to obtain the best chromosome which is then used to perform K-means clustering (termed NicheClust). SSE, DB-index, PBM-index, and COSEC are used as fitness functions for NGA. The experimental results demonstrate that NicheClust has high performance and efficiency for three GPS location datasets. [ABSTRACT FROM AUTHOR]

In order to solve virtual problems of cloud computing such as efficiency, delay and attenuation performance. An approach based on PSO ( particle swarm optimization ) is presented in the paper. According to the advantage of cloud computing, some properties of virtual resources( virtual machine; VM) are described and defined, and QoSs ( quality of services ) and computing methods of VM are also defined and set; alter that balance relation and maximum profits of cloud users ( U) and VM providers,a VM's operate approach is described, which is sure to be distributed and retrieved the VM; And we employ and design PSO to accomplish profits balance optimization lor U and VM on the basis of the research results, a cloud player is desgined and realized, compare with a web player, the approach show that is effective, and the result improved at least 10%. Meanwhile, PSO-based approach is more effective than BIP( binary interger programming)-based QoS constrained allocation. [ABSTRACT FROM AUTHOR]

In order to effectively extract features and improve classification accuracy for hyperspectral remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization (PSO) algorithm, convolutional neural network (CNN), and extreme learning machine (ELM) are fully utilized to propose an innovative classification method of HRSIs (IPCEHRIC) in this paper. In the IPCEHRIC, an enhanced PSO algorithm (CWLPSO) is developed by improving learning factor and inertia weight to improve the global optimization performance, which is employed to optimize the parameters of the CNN in order to construct an optimized CNN model for effectively extracting the deep features of HRSIs. Then, a feature matrix is constructed and the ELM with strong generalization ability and fast learning ability is employed to realize the accurate classification of HRSIs. Pavia University data and actual HRSIs after Jiuzhaigou M7.0 earthquake are applied to test and prove the effectiveness of the IPCEHRIC. The experiment results show that the optimized CNN can effectively extract the deep features from HRSIs, and the IPCEHRIC can accurately classify the HRSIs after Jiuzhaigou M7.0 earthquake to obtain the villages, bareland, grassland, trees, water, and rocks. Therefore, the IPCEHRIC takes on stronger generalization, faster learning ability, and higher classification accuracy. [ABSTRACT FROM AUTHOR]

Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory. [ABSTRACT FROM AUTHOR]

K-means clustering is an important and popular technique in data mining. Unfortunately, for any given dataset (not knowledge-base), it is very difficult for a user to estimate the proper number of clusters in advance, and it also has the tendency of trapping in local optimum when the initial seeds are randomly chosen. The genetic algorithms (GAs) are usually used to determine the number of clusters automatically and to capture an optimal solution as the initial seeds of K-means clustering or K-means clustering results. However, they typically choose the genes of chromosomes randomly, which results in poor clustering results, whereas a generally selected initial population can improve the final clustering results. Hence, some GA-based techniques carefully select a high-quality initial population with a high complexity. This paper proposed an adaptive GA (AGA) with an improved initial population for K-means clustering (SeedClust). In SeedClust, which is an improved density estimation method and the improved K-means++ are presented to capture higher quality initial seeds and generate the initial population with low complexity, and the adaptive crossover and mutation probability is designed and is then used for premature convergence and to maintain the population diversity, respectively, which can automatically determine the proper number of clusters and capture an improved initial solution. Finally, the best chromosomes (centers) are obtained and are then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on low-dimensional taxi GPS (Global Position System) data sets demonstrate that SeedClust has a higher performance and effectiveness. [ABSTRACT FROM AUTHOR]

Hyperspectral images contain abundant spectral and spatial information of the surface of the earth, but there are more difficulties in processing, analyzing, and sample-labeling these hyperspectral images. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced to propose a sample labeling method based on neighborhood information and priority classifier discrimination. A new hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. The multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier discrimination are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new classification method based on semi-supervised learning is proposed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results have demonstrated that the proposed classification method is able to gain a higher classification accuracy, a stronger timeliness, and the generalization ability.

Objective: To assess prognostic aspects of treatment modalities for cases of hepatocellular carcinoma (HCC) with portal vein tumor thrombi (PVTT)., Method: 121 treated cases were retrospectively divided into five groups: 1 (liver transplantation); 2 (transcatheter arterial chemoembolization); 3 (hepatectomy plus thrombectomy); 4 (hepatectomy plus thrombectomy combined with adjuvant chemobiotherapy via portal vein); and 5 (conservative treatment). The Kaplan-Meier method with difference in survival estimated by Log-rank test was used to compare between groups., Result: Groups 1-5 had a significantly differing median survival times of 7, 7, 10, 16, 3 months (P<0.05), respectively. One- and three-year survival rates were 30.0% and 10.0%, 20.0% and 0.0%, 47.0% and 22.0%, 70% and 20%, and 12% and 4%., Conclusion: Surgical resection combined with adjuvant chemotherapy via the portal vein is an effective and safe treatment modality for hepatocellular carcinoma with portal vein tumor thrombi.