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2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)

作者： Asif Hassan Yanxiao Zhao Lina Pu Guodong Wang Hui Sun Robb M Winter South Dakota School of Mines and Technology faculty in Information Engineering and Automation Civil Aviation University of China

Wireless smart meter network has attracted extensive research interests for its capability on intelligent control and monitor the power consumption in residential as well as commercial *** smart meter network constitutes with a number of smart meters to sense the electricity or gas consumption,data aggregation point(DAP) to collect data from smart meters,and utility center to gather all data from *** this work,we investigate the clustering and optimal DAP placement problem in the multi-hop smart meter *** representative clustering algorithms,namely k-means,self-organizing map,and fuzzy c-means,are evaluated and compared in terms of multi-hop shortest path distance(MSPD),size of clusters and computation *** results indicate that the hard assignment based methods,k-means and self-organizing,achieve similar performance whereas the soft assignment based fuzzy c-means falls behind with longer maximum MSPD and higher complexity.

关键词： Wireless smart meter network data aggregation point placement clustering algorithm

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Adapt DB-PSO patterns clustering algorithms and its applications in image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第23期75卷 15327-15339页

作者： Chen, Hua-Ching Feng, Hsuan-Ming Lin, Te-Hui Chen, Ching-Yi Zha, Yu-Xiang Natl Quemoy Univ Dept Comp Sci & Informat Engn Kinmen 892 Taiwan Ming Chuan Univ Dept Informat & Telecommun Engn Taoyuan Taiwan

clustering algorithm is a crucial step before to analysis object's feature in image applications. The adapt DB-PSO patterns clustering algorithms (ADPCA) combined the particle swarm optimization (PSO) clustering algorithm and adapt DB_index measuring methodology to efficiently decide the real number of clusters, cluster centers, and then to recognize the correct catalog even if there are exiting some cases in various shapes, multi-dimension, real life training patterns and image datasets. In general, the PSO is adapted for dealing complex and global optimization problems. The population-based evolutional PSO learning algorithm with the self-adapt mathematic index can fit the data vibration to perform the real criterion of homogeneity of neighboring pixels in many image vision and understanding cases. Owing to the purpose of generating automatic clustering algorithms, the specific fitness function contains the DB_validity measure to significantly improve resolutions of spatial information among the given training patterns. The computation of image DB_index is delivered to retrieve the specific objects by evaluating the characters of given patterns. The novel ADPCA actually indicate the homogeneity region of interesting pictures and eliminate small pieces of elements by the supports of DB index measure, which can be used to dynamically compute the maximal similarity and small difference of the discussed image patterns. Several artificial datasets include the three-dimensional dataset with five spherical clusters, two-dimensional patterns with three different sizes circles, one Chtree Fractal image patterns, one real life IRIS data and one grey level image data, which are given as training patterns to demonstrate the adaptation and efficiency of the ADPCA learning method. It presents that ADPCA determine the correct clustering number and suitable cluster position in different data clustering examples. Two image segmentation applications also show that ADPCA can achieve

关键词： Particle swarm optimization DB validity clustering algorithm Image segmentation

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A robust adaptive particle swarm optimization for clustering analysis based on steepest descent method

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第1期32卷 23-33页

作者： Sun, Zhichao He, Ying Wu, Junjie Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Dept Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Res Inst Elect Sci & Technol Chengdu Peoples R China

Possibilistic fuzzy c-means (PFCM) is used for solving the problem of data classification. It relies on initial cluster centers set by users that are lack of theoretical supporting. Inappropriate initial values may result in deviation of cluster centers. In this paper, A Robust Adaptive Particle Swarm Optimization based on steepest descent method is proposed to solve the problem of initialization and improving the performance of clustering. Combined with clustering algorithm, particle swarm optimization (PSO) possesses the good robustness to noises. Furthermore, since traditional PSO is inefficient when searching in the complex nonlinear hyperspace. Steepest descend method is applied to adaptively adjusting parameters. Moreover, optimum combined position is used to update the current information of each particle, which can discover more useful information lies in personal optimal experience and global optimal experience. The performance of proposed algorithm are tested in numerical simulations. The effectiveness, accuracy and stability of the new model are verified by simulations both with and without noises.

关键词： clustering algorithm particle swarm optimization steepest descend method self-adaptive optimal combination

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A netting method for clustering-simplified neutrosophic information

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SOFT COMPUTING 2017年第24期21卷 7571-7577页

作者： Ye, Jun Shaoxing Univ Dept Elect & Informat Engn 508 Huancheng West Rd Shaoxing 312000 Zhejiang Peoples R China

To simplify existing clustering algorithms of simplified neutrosophic sets (NSs) (including single-valued NSs and interval NSs), the paper proposes a netting method for clustering-simplified neutrosophic data based on new association coefficients of simplified NSs. In the clustering algorithms, we firstly present new association coefficients between simplified NSs, including an association coefficient between single-valued NSs and an association coefficient between interval NSs. Then, a netting clustering method is presented based on the association coefficient matrix of simplified NSs to cluster simplified neutrosophic data. Finally, an actual example is provided to illustrate the effectiveness and rationality of the proposed netting clustering method under a simplified neutrosophic environment.

关键词： Netting method clustering algorithm Association coefficient Association coefficient matrix Simplified neutrosophic set

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NC-Link: A New Linkage Method for Efficient Hierarchical clustering of Large-Scale Data

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IEEE ACCESS 2017年 5卷 5594-5608页

作者： Jeon, Yongkweon Yoo, Jaeyoon Lee, Jongsun Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Stanford Univ Dept Neurol & Neurol Sci Stanford CA 94305 USA

In various disciplines, hierarchical clustering (HC) has been an effective tool for data analysis due to its ability to summarize hierarchical structures of data in an intuitive and interpretable manner. A run of HC requires multiple iterations, each of which needs to compute and update the pairwise distances between all intermediate clusters. This makes the exact algorithm for HC inevitably suffer from quadratic time and space complexities. To address large-scale data, various approximate/parallel algorithms have been proposed to reduce the computational cost of HC. However, such algorithms still rely on conventional linkage methods (such as single, centroid, average, complete, or Ward's) for defining pairwise distances, mostly focusing on the approximation/parallelization of linkage computations. Given that the choice of linkage profoundly affects not only the quality but also the efficiency of HC, we propose a new linkage method named NC-link and design an exact algorithm for NC-link-based HC. To guarantee the exactness, the proposed algorithm maintains the quadratic nature in time complexity but exhibits only linear space complexity, thereby allowing us to address million-object data on a personal computer. To underpin the extensibility of our approach, we show that the algorithmic nature of NC-link enables single instruction multiple data (SIMD) parallelization and subquadratic-time approximation of HC. To verify our proposal, we thoroughly tested it with a number of large-scale real and synthetic data sets. In terms of efficiency, NC-link allowed us to perform HC substantially more space efficiently or faster than conventional methods: compared with average and complete linkages, using NC-link incurred only 0.7%-1.75% of the memory usage, and the NC-link-based implementation delivered speedups of approximately 3.5 times over the centroid and Ward's linkages. With regard to clustering quality, the proposed method was able to retrieve hierarchical structures fro

关键词： clustering algorithm data mining machine learning

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Segmentation data visualizing and clustering

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第1期76卷 1531-1552页

作者： Khlif, Ayman Mignotte, Max Univ Montreal DIRO Fac Arts & Sci Montreal PQ H3C 3J7 Canada

Browsing, searching and retrieving images from large databases based on low level color or texture visual features have been widely studied in recent years but are also often limited in terms of usefulness. In this paper, we propose a new framework that allows users to effectively browse and search in large image database based on their segmentation-based descriptive content and, more precisely, based on the geometrical layout and shapes of the different objects detected and segmented in the scene. This descriptive information, provided at a higher level of abstraction, can be a significant and complementary information which helps the user to browse through the collection in an intuitive and efficient manner. In addition, we study and discuss various ways and tools for efficiently clustering or for retrieving a specific subset or class of images in terms of segmentation-based descriptive content which can also be used to efficiently summarize the content of the image database. Experiments conducted on the Berkeley Segmentation Datasets show that this new framework can be effective in supporting image browsing and retrieval tasks.

关键词： Berkeley dataset clustering algorithm Entropy Database browsing and retrieving images Hierarchical clustering K-means Multidimensional visualization Query-by-drawing Segmentation data clustering Descriptive content based image classification Variation of information Visualization of image databases

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Sales forecasting by combining clustering and machine-learning techniques for computer retailing

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NEURAL COMPUTING & APPLICATIONS 2017年第9期28卷 2633-2647页

作者： Chen, I-Fei Lu, Chi-Jie Tamkang Univ Dept Management Sci New Taipei Taiwan Chien Hsin Univ Sci & Technol Dept Ind Management Taoyuan 32097 Taiwan

Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing.

关键词： Sales forecasting Computer retailing clustering algorithm Machine learning

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Scale space clustering evolution for salient region detection on 3D deformable shapes

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PATTERN RECOGNITION 2017年 71卷 414-427页

作者： Wang, Xupeng Sohel, Ferdous Bennamoun, Mohammed Guo, Yulan Lei, Hang Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Western Australia Sch Comp Sci & Software Engn Perth WA Australia Murdoch Univ Sch Engn & Informat Technol Perth WA Australia Natl Univ Def Technol Coll Elect Sci & Engn Changsha Hunan Peoples R China

Salient region detection without prior knowledge is a challenging task, especially for 3D deformable shapes. This paper presents a novel framework that relies on clustering of a data set derived from the scale space of the auto diffusion function. It consists of three major techniques: scalar field construction, shape segmentation initialization and salient region detection. We define the scalar field using the auto diffusion function at consecutive time scales to reveal shape features. Initial segmentation of a shape is obtained using persistence-based clustering, which is performed on the scalar field at a large time scale to capture the global shape structure. We propose two measures to assess the clustering both on a global and local level using persistent homology. From these measures, salient regions are detected during the evolution of the scalar field. Experimental results on three popular datasets demonstrate the superior performance of the proposed framework in region detection. (C) 2017 Published by Elsevier Ltd.

关键词： Deformable shape segmentation Salient region detection Diffusion geometry clustering algorithm Persistent homology

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Coverage-Based clustering and Scheduling Approach for Test Case Prioritization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2017年第6期E100D卷 1218-1230页

作者： Fu, Wenhao Yu, Huiqun Fan, Guisheng Ji, Xiang East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.

关键词： regression testing test case prioritization clustering algorithm scheduling algorithm

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clustering-Based Geometrical Structure Retrieval of Man-Made Target in SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2017年第3期14卷 279-283页

作者： Wu, Jiani Chen, Yongguang Dai, Dahai Chen, Siwei Wang, Xuesong Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effe Changsha 410073 Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

In synthetic aperture radar (SAR) images, scattering centers (SCs) from the same geometric structure of the man-made target usually have the same scattering type and similar coordinates. Inspired by this observation, a novel clustering-based geometrical structure retrieval (C-GSR) method is proposed to estimate the geometrical structure of targets by clustering SCs according to their types and coordinates. The C-GSR method considers each peak in a SAR image as a single SC and extracts both frequency and polarization features for classification. Then, SCs are efficiently clustered using the density-distance-based clustering algorithm. Finally, the geometrical structure corresponding to each canonical scatterer can be retrieved by computing the coordinates of SCs associated with the corresponding cluster. Experimental results have demonstrated the feasibility and accuracy of the proposed C-GSR method.

关键词： clustering algorithm geometrical structure retrieval scattering centers (SC) classification synthetic aperture radar (SAR) images

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