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Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral clustering

IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 11172-11186页

作者： Wan, Yuting Ma, Ailong Zhang, Liangpei Zhong, Yanfei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Nat Resources Remote Sens Wuhan 430079 Peoples R China

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

关键词： Optimization Remote sensing clustering algorithms Linear programming Indexes Task analysis Genetic algorithms Image data clustering knee point-based selection multiobjective optimization remote sensing sine cosine algorithm (SCA) spatial information

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A Novel Hybrid clustering Approach Based on Black Hole Algorithm for Document clustering

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IEEE ACCESS 2022年 10卷 97310-97326页

作者： Malik, Fazila Khan, Salabat Rizwan, Atif Atteia, Ghada Samee, Nagwan Abdel COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Jeju Natl Univ Dept Comp Engn Jeju Si 63243 Jeju Special Se South Korea Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia Misr Univ Sci & Technol Comp Engn Dept Giza 12511 Egypt

In information retrieval and text mining, document clustering is a big challenge because the amount of document collection has been increasing, day by day. The problem of clustering is NP-hard, use of meta-heuristic algorithms to solve these problems could be an effective method. When the solution space is large, traditional methods are unable to find a solution in a reasonable amount of time. K-means is a heuristic clustering algorithm, two main issues with heuristic algorithms are the early convergence and trapping in local optima. Moreover, finding the right number of clusters is one of the main drawbacks of the k-means algorithm. The correct value of k is always confusing, different researchers used different methods to solve this problem. To overcome these mentioned problems, this study presents a novel Hybrid approach for document clustering. One of the challenges in existing BH algorithm is the input data type. Recently, the algorithm was only accepting textual data. Another flaw in the existing model is that it doesn't choose how many clusters k to form automatically, and the centroids are chosen at random in it. In this paper, we have constructed a Hybrid cluster identification approach which consists of the Elbow method and Silhouette score for cluster k identification. This paper mainly offers three novel combination of model to represent text documents, namely i) K-mean++ - BH + TF-IDF with fix k ii) K-mean++ - BH + W2V with fix k iii) Hybrid Black Hole with automated k. The proposed improvements have validated on the document clustering problem. Cluster analysis based on two evaluation measures, external (Purity) and internal measures (Silhouette score) are used to report the findings. Experiments have been carried out on the four al-phanumeric datasets (Doc50, Reuters, WebKB and News20) as well as on two numeric datasets (Iris and Wine) respectively. The complete result analysis is reported in detail with respect to each research contribution to compar

关键词： clustering algorithms Heuristic algorithms clustering methods Text analysis Data mining Task analysis Document clustering black hole algorithm k-mean data mining comparative analysis

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GRACGE: Graph Signal clustering and Multiple Graph Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 2015-2030页

作者： Yuan, Yanli Soh, De Wen Yang, Xiao Guo, Kun Quek, Tony Q. S. Singapore Univ Technol & Design Singapore Singapore Xidian Univ State Key Lab Integrated Serv Inst Informat Sci Xian 710071 Shaanxi Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

In graph signal processing (GSP), complex datasets arise from several underlying graphs and in the presence of heterogeneity. Graph learning from heterogeneous graph signals often results in challenging high-dimensional multiple graph estimation problems, and prior information regarding which graph the data was observed is typically unknown. To address the above challenges, we develop a novel framework called GRACGE (GRAph signal clustering and multiple Graph Estimation) to partition the graph signals into clusters and jointly learn the multiple underlying graphs for each of the clusters. GRACGE advocates a regularized EM (rEM) algorithm where a structure fusion penalty with adaptive regularization parameters is imposed on the M-step. Such a penalty can exploit the structural similarities among graphs to overcome the curse of dimensionality. Moreover, we provide a non-asymptotic bound on the estimation error of the GRACGE algorithm, which establishes its computational and statistical guarantees. Furthermore, this theoretical analysis motivates us to adaptively re-weight the regularization parameters. With the adaptive regularization scheme, the final estimates of GRACGE will geometrically converge to the true parameters within statistical precision. Finally, experimental results on both synthetic and real data demonstrate the performance of the proposed GRACGE algorithm.

关键词： Laplace equations Signal processing algorithms clustering algorithms Estimation error Topology Convergence Learning systems Multiple graph learning graph signal processing (GSP) signal clustering regularized EM algorithm nonasymptotic statistical analysis

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Maximum Joint Probability With Multiple Representations for clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4300-4310页

作者： Zhang, Rui Zhang, Hongyuan Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Classical generative models in unsupervised learning intend to maximize p(X). In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution p(X) to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. K-means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model.

关键词： Kernel Unsupervised learning Data models clustering algorithms Task analysis Feature extraction Computational modeling clustering maximum joint probability multikernel learning multiview learning unsupervised learning

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A Fast clustering Algorithm for Modularization of Large-Scale Software Systems

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第4期48卷 1451-1462页

作者： Teymourian, Navid Izadkhah, Habib Isazadeh, Ayaz Univ Tabriz Dept Comp Sci Tabriz *** Iran

A software system evolves over time in order to meet the needs of users. Understanding a program is the most important step to apply new requirements. clustering techniques through dividing a program into small and meaningful parts make it possible to understand the program. In general, clustering algorithms are classified into two categories: hierarchical and non-hierarchical algorithms (such as search-based approaches). While clustering problems generally tend to be NP-hard, search-based algorithms produce acceptable clustering and have time and space constraints and hence they are inefficient in large-scale software systems. Most algorithms which currently used in software clustering fields do not scale well when applied to large and very large applications. In this paper, we present a new and fast clustering algorithm, FCA, that can overcome space and time constraints of existing algorithms by performing operations on the dependency matrix and extracting other matrices based on a set of features. The experimental results on ten small-sized applications, ten folders with different functionalities from Mozilla Firefox, a large-sized application (namely ITK), and a very large-sized application (namely Chromium) demonstrate that the proposed algorithm achieves higher quality modularization compared with hierarchical algorithms. It can also compete with search-based algorithms and a clustering algorithm based on subsystem patterns. But the running time of the proposed algorithm is much shorter than that of the hierarchical and non-hierarchical algorithms. The source code of the proposed algorithm can be accessed at https://***/SoftwareMaintenanceLab.

关键词： clustering algorithms Software algorithms Search problems Software systems Semantics Software architecture Software clustering software modularization software maintenance software comprehension architecture recovery

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Fuzzy-Based Multi-Layered clustering and ACO-Based Multiple Mobile Sinks Path Planning for Optimal Coverage in WSNs

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IEEE SENSORS JOURNAL 2022年第7期22卷 7277-7287页

作者： Khedr, Ahmed M. Al Aghbari, Zaher Khalifa, Banafsj E. Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm;meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime.

关键词： Sensors Wireless sensor networks Delays clustering algorithms Energy consumption Data collection Trajectory Multi-layered clustering fuzzy c-means mobile collectors (MCs) wireless sensor network (WSN) ACO route planning

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Feature Saliencies in Asymmetric Hidden Markov Models

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 3586-3600页

作者： Puerto-Santana, Carlos Larranaga, Pedro Bielza, Concha Aingura IIoT Donostia San Sebastian 20009 Spain Univ Politecn Madrid Dept Inteligencia Artificial ETS Ingenieros Informat Madrid 28660 Spain

Many real-life problems are stated as nonlabeled high-dimensional data. Current strategies to select features are mainly focused on labeled data, which reduces the options to select relevant features for unsupervised problems, such as clustering. Recently, feature saliency models have been introduced and developed as clustering models to select and detect relevant variables/features as the model is learned. Usually, these models assume that all variables are independent, which narrows their applicability. This article introduces asymmetric hidden Markov models with feature saliencies, i.e., models capable of simultaneously determining during their learning phase relevant variables/features and probabilistic relationships between variables. The proposed models are compared with other state-of-the-art approaches using synthetic data and real data related to grammatical face videos and wear in ball bearings. We show that the proposed models have better or equal fitness than other state-of-the-art models and provide further data insights.

关键词： Hidden Markov models Data models Computational modeling Bayes methods Markov processes Feature extraction clustering algorithms Asymmetric information Bayesian networks embedded feature selection feature saliencies feature subset selection (FSS) hidden Markov models (HMMs)

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Hilbert-Ordering Based clustering of Complex-Excitations Linear Arrays

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第8期70卷 6751-6762页

作者： Benoni, Arianna Rocca, Paolo Anselmi, Nicola Massa, Andrea Univ Trento Dept Civil Environm & Mech Engn DICAM ELEDIA Res Ctr ELEDIA UniTN I-38123 Trento Italy CNIT Univ Trento Res Unit I-38123 Trento Italy Xidian Univ ELEDIA Res Ctr ELEDIA XIDIAN Xian 710071 Shaanxi Peoples R China ELEDIA Res Ctr ELEDIA UESTC UESTC Sch Sci & Engn Chengdu 611731 Peoples R China Tsinghua Univ ELEDIA Res Ctr ELEDIA TSINGHUA Beijing 100084 Haidian Peoples R China

The clustering of linear phased arrays (PAs) with complex weights is discussed. By exploiting the locality-preservation property of the Hilbert curve, the dimensionality of the problem at hand has been first reduced, and then, a simple clustering algorithm has been applied to maximize the matching of the radiated pattern with a reference one. Both contiguous and noncontiguous partitions of the Hilbert-ordered list of complex excitations have been evaluated to effectively sample the solution space of clustered solutions. A set of representative results, including reference PAs affording steered pencil and shaped beams, are reported for validation purposes as well as to point out the effectiveness of the proposed approach in comparison with state-of-the-art k-means algorithms as well.

关键词： Pattern matching Phased arrays Costs clustering algorithms Linear antenna arrays Cost function Wavelength measurement Arbitrary-shaped pattern beams complex excitations Hilbert curve linear phased array (PA) power pattern matching subarraying

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Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10228-10239页

作者： Wang, Qi Liu, Ran Chen, Mulin Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Peoples R China

Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we propose a robust rank constrained sparse learning (RRCSL) method in this article. The L2,1-norm is adopted into the objective function of sparse representation to learn the optimal graph with robustness. To preserve the data structure, we construct an initial graph and search the graph within its neighborhood. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator, and the final results are obtained without additional postprocessing. In addition, the proposed method cannot only be applied to single-view clustering but also extended to multiview clustering. Plenty of experiments on synthetic and real-world datasets have demonstrated the superiority and robustness of the proposed framework.

关键词： Optimization Linear programming clustering algorithms Laplace equations Sparse matrices Robustness Data structures Graph clustering graph learning machine learning multiview clustering sparse representation

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Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D Mesh

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第8期30卷 5553-5565页

作者： Zhuang, Jiafu Zeng, Pan Zhuang, Wei Guo, Xiaoyu Liu, Peizhong Quanzhou Normal Univ Sch Phys & Informat Engn Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Sch Med Quanzhou 362021 Fujian Peoples R China Univ Manchester Dept Social Stat Manchester M13 9PL England Quanzhou Normal Univ Coll Oceanol & Food Sci Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Coll Engn Quanzhou 362021 Fujian Peoples R China Quanzhou Med Coll Sch Med Quanzhou 362021 Fujian Peoples R China

The analysis of 3D meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2D images to 3D meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GDSV). The key to the GDSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GDSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training;and be applied to tasks such as shape classification, part segmentation, and 3D scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

关键词： Three-dimensional displays clustering algorithms Shape Task analysis Manifolds Deep learning Training 3D mesh segmentation 3D shape learning deep clustering graph neural network

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