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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Lasek, Piotr Lasek, Krzysztof University of Rzeszów Pigonia 1 Rzeszów35-310 Poland

clustering is a widely utilized data mining technique aimed at discovering unknown patterns in datasets, typically performed in an unsupervised manner. However, the incorporation of external knowledge, transforming it into a semi-supervised process, has shown promising results in improving clustering performance. This study explores the application of feature constraints in clustering algorithms, specifically focusing on relative constraints, which are effective in embedding domain knowledge. We introduce a method to use relative constraints as features, integrating them into the Neighborhood-Based clustering algorithm. Our experiments demonstrate the impact of these constraints on clustering accuracy and efficiency using various benchmark datasets. The results indicate that the proposed method enhances clustering quality by effectively incorporating domain-specific constraints, offering a novel approach to semi-supervised clustering. © 2024 The Authors.

关键词： clustering algorithms

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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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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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Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4918-4934页

作者： Deng, Qinwen Zhang, Yangwen Li, Mo Zhang, Songyang Ding, Zhi Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Univ Louisiana Lafayette Dept Math Lafayette LA 70504 USA Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

Graph spectral analysis has emerged as an important tool to extract underlying structures among data samples. Central to graph signal processing (GSP) and graph neural networks (GNN), graph spectrum is often derived via eigen-decomposition (ED) of graph representation (adjacency/Laplacian) matrix. Many real-world applications feature dynamic graphs whose representation matrix size varies over time. Such evolving graph usually shares part of the same structures with the previous graphs. We consider efficient ways to estimate the $K$ dominant eigenvectors of the graph representation matrix. We focus on an iterative ED algorithm for low-rank symmetric matrices to update the top $K$ eigen-pairs of the representation matrix for a graph with increasing node size. To accommodate the growing graph size, we propose two Incremental ED algorithms for Low Rank symmetric matrices (ILRED) based on an iterative eigen-updating strategy. We also provide analysis on the resulting error performance, computational complexity and memory usage to showcase the efficiency of ILRED. The experimental results in both synthetic and real-world datasets with the context of spectral clustering and graph filtering validate the power of the proposed ILRED algorithms.

关键词： Signal processing algorithms Symmetric matrices Approximation algorithms Spectral analysis Filters Eigenvalues and eigenfunctions clustering algorithms Eigen-decomposition eigen-updating graph signal processing graph spectral analysis

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An Effective VNS Algorithm for k-Medoids clustering Problem

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IEEE LATIN AMERICA TRANSACTIONS 2022年第5期20卷 710-717页

作者： de Moura Brito, Jose Andre Semaan, Gustavo Silva Fadel, Augusto Cesar Escola Nacl Ciencias Estat Brasil ENCE IBGE Brasilia DF Brazil Inst Noroeste Fluminense Educ Super INFES UFF Niteroi RJ Brazil Inst Brasileiro Geog & Estat IBGE Brasilia DF Brazil

This paper proposes an algorithm based on VNS metaheuristcs for k-medoids clustering, which is a NP-hard optimization problem. The VNS algorithm was applied in fifty data bases (instances) with small, medium, and large sizes, considering the number of clusters between 2 and 7. The obtained results from these experiments show the effectiveness of this approach, comparing it with nine other related clustering algorithms and an optimization formulation. Furthermore, we found that our algorithm obtained the optimal solutions for the vast majority of the cases.

关键词： clustering algorithms Optimization IEEE transactions Programming Indexes Genetic algorithms Chemicals k-medoids clustering VNS mathematical programming

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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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FedOrbit: Energy Efficient Federated Learning for Orbital Edge Computing Using Block Minifloat Arithmetic

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 3657-3671页

作者： Jabbarpour, Mohammad Reza Javadi, Bahman Leong, Philip H. W. Calheiros, Rodrigo N. Boland, David Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2000 Australia Univ Sydney Sch Elect & Informat Engn Camperdown NSW 2050 Australia

Low Earth Orbit (LEO) satellite constellations have diverse applications, including earth observation, communication services, navigation, and positioning. These constellations have evolved into a valuable data source;however, their use in a ground station (GS) for analysis via machine learning algorithms presents challenges due to constraints on power consumption, communication bandwidth, and onboard computing capabilities. While the combination of Federated Learning (FL) and Orbital Edge Computing has been employed to address these challenges, its heavy reliance on the GS for model aggregation and edge resource limitations remains a research challenge. This article presents FedOrbit, a novel energy-efficient and decentralised FL method to optimise communication with the GS and reduce power consumption. FedOrbit utilises reinforcement learning for cluster formation, satellite visiting patterns for master satellite selection, and block minifloat arithmetic for power reduction. Extensive performance evaluation under Walker Delta-based LEO constellation configurations and different datasets reveals that FedOrbit can maintain high accuracy while significantly reduce communication demand, power consumption and training time in comparison to state-of-the-art FL approaches. The proposed technique can also reduce the training time by 5x compared with the centralised FL approaches. In addition, the utilisation of block minifloat representation as low-precision arithmetic enhanced the energy consumption by 3.5x compared with the single-precision (FP32) format.

关键词： Training Satellites Quantization (signal) Orbits Servers Computational modeling clustering algorithms Power demand Low earth orbit satellites Arithmetic Block minifloat energy consumption federated learning low-earth orbit orbital edge computing

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clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2087-2098页

作者： Wu, You Zhao, Quanhua Shen, Zhaoyu Li, Yu Liaoning Tech Univ Sch Geomat Fuxin 123000 Peoples R China

To extract complicated road network from remote sensing images on urban scenes, this article presents a clustering point process (CPP) based network topology structure constrained road extraction algorithm. Firstly, the CPP is constructed to model the feature points, such as endpoints, bends, and crossroads in a road system. Based on that, an initial network topology structure is constructed by connecting the points with lines. Then, according to the network structure characteristic and the spectral characteristic of road, a network topology structure constraining model and a spectral measurement constraining model are constructed, respectively. By combining the models above, a road extraction model is built under the framework of Bayes' theorem. Finally, to simulate from the road extraction model and extract an optimal road network, a solution strategy, reversible jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm with related transfer operations, is designed according to the CPP and network topology structure. Several high-resolution remote sensing images on urban scenes are tested. According to a buffer evaluation method, and compared with the comparing algorithms, accuracy and extraction rate of results from the proposed algorithm are increased by 10.86% and 8.75% on average, respectively. It is proved that the proposed algorithm can extract the complicated road network effectively.

关键词： Roads Feature extraction Network topology Remote sensing Junctions clustering algorithms Bending clustering point process (CPP) feature points network topology structure reversible jump Markov chain Monte Carlo (RJMCMC) urban road extraction

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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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