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Constrained spectral clustering is an important area with many applications. However, most previous work has only been applied to relatively small data sets: graphs with thousands of points. This prevents this work from being applied to the large data sets found in application domains such as medical imaging and document data. Recent work on constrained and unconstrained spectral clustering has explored...
Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
In this paper a Genetic Algorithm (GA) is used to partition a distribution network with the aim to minimize the energy exchange among the microgrids (i.e. maximize self-consumption) in presence of distributed generation. The proposed GA is tested on the IEEE prototypical network PG & E 69-bus. The microgrid partitioning is tested over a period of one year with hourly sampled data of real household...
This paper considers the optimal energy generation problem for hierarchical system, which consists of multi-cluster power system. In particular, consensus-based distributed hierarchical coordination algorithm is proposed to meet the power generation/demand balance. By using Lagrangian-based approach, we show that the optimization problem for the hierarchical system can be separated into each layer's...
To overcome the deficiencies of traditional K-means algorithm whose clustering effect and stability are easily affected by the initial clustering centers, this paper proposes an initial clustering center selection algorithm based on Max-Min criterion and FLANN. The algorithm firstly identifies K farthest objects, and then finds out the k nearest neighbors of K objects respectively. Finally, take the...
Glowworm Swarm Optimization Algorithm (GSO) is one of new swarm intelligence optimization algorithms in recent years. Its main idea comes from the cooperative behavior source among individuals during the process of courtship and foraging. In this paper, in order to improve convergence speed in the late iteration, avoid the algorithm falling into local optimum, and reduce isolated nodes, the Adaptive...
We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches, therefore, it can achieve fast global optimization. Moreover, the RE algorithm is easy to implement and successful in high-dimensional...
With the explosive growth of user load data in power consumption information collection and load control systems, traditional computing frameworks and methods are faced with tremendous computational pressure when dealing with massive user load clustering and carrying out load characteristic analysis. In this paper, with a view to increasing accuracy and computational power of graphic process unit...
Large scale monitoring systems require reliable and efficient in-network information extraction mechanisms able to effectively track events at the field level. The study of consensus algorithms for distributed data processing has gained a lot of interest in the last decade. Average consensus algorithms used for decentralized sensor fusion in wireless sensor networks, iteratively compute the global...
Data Clustering in Data Mining is a domain which never gets out of focus. Clustering a data was always an easy task but achieving the required accuracy, precision and performance was never so easy. K means being an archaic clustering algorithm got tested and experimented thousands of times with variety of datasets and other combination of algorithm due to its robustness and simplicity but what this...
A new class of cooperative bat-inspired consensus protocols are proposed and analyzed in this paper. Motivated by the bat searching algorithm in swarm intelligence, the proposed bat consensus protocols have the outstanding feature of achieving convergent agreement of bat states toward a suggested direction, a feature also which happens to be solving a separate optimization problem. Hence, such protocols...
The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering, where the spectrum of the Laplacian is recovered following a constrained optimization problem that...
We compare the performance of three parallel clustering algorithms: Canopy, K-means and fuzzy K-means in real cluster environments. By constructing cluster platform of different scale, we compare these algorithms from three metrics: run time, speedup and sizeup. Experimental results show that: (1) if both the data set and the number of nodes in the cluster are the same, both the runtime and the sizeup...
To solve the overlap problem in node selection process in order to fill the existing routing algorithms deficiencies, This paper designs a traditional Gossiping routing algorithm based on SPSO. At the same time, the paper gives the content and design steps of this algorithm. The simulation results show that the algorithm can effectively solve the problem of node selection.
In recent years, clustering has become a hotspot in the field of data mining, as one of the key technologies of getting data distribution and observing the characteristics of class. However, some clustering algorithms depend on the selection of initial clustering centers, and the clustering results easily fall into local optimal. To solve the above problem, the paper integrates differential evolution...
This thesis tries to analyze the drawbacks and shortages of the present ant-based text clustering algorithms (Z-ACTCA algorithm in short). The author attempts to improve the ant-based text clustering algorithms from the following three aspects: text similarity calculation, iterations termination condition as well as parametric adaption. Meanwhile, preprocession will be done on the two-dimensional...
Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. Different from the existing works, we prove that the algorithm converges to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex...
Wireless Sensor Network (WSN) is an emerging application that has proved to be very effective due to its wide application and so has become very prominent various industries and research WSN's life is improved through clustering-based routing. Operation and network life are controlled by a large deployed sensor network whose major characteristic is self-organization and energy efficiency. The area...
This paper discusses a deterministic clustering approach to capacitated resource allocation problems. In particular, the Deterministic Annealing (DA) algorithm from the data-compression literature, which bears a distinct analogy to the phase transformation under annealing process in statistical physics, is adapted to address problems pertaining to clustering with several forms of size constraints...
For large-scale, high-dimensional, sparse categorical data clustering, compared with the traditional clustering algorithm, CLOPE has a great improvement in the quality of clustering and running speed. However, CLOPE algorithm itself also has some defects in clustering quality stability and does not distinguish the attribute clustering contribution between dimensions, besides, it needs to specify rejection...
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