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A Metaheuristic Search Algorithm Based on Sampling and Clustering

IEEE ACCESS 2024年 12卷 15493-15508页

作者： Harita, Maria Wong, Alvaro Suppi, Remo Rexachs, Dolores Luque, Emilio Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst Bellaterra 08193 Spain

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA's parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

关键词： Clustering algorithms Optimization Heuristic algorithms Metaheuristics Search problems Benchmark testing partitioning algorithms Benchmarks heuristic methods Knapsack problem Montecarlo and clustering methods optimization

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Community Detection in Social Networks Using a Local Approach Based on Node Ranking

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IEEE ACCESS 2024年 12卷 92892-92905页

作者： Sheykhzadeh, Jafar Zarei, Bagher Soleimanian Gharehchopogh, Farhad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh *** Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Shabestar Branch Shabestar *** Iran

Community detection is crucial for analyzing the structure of social networks and extracting hidden information from them. The goal is to find groups of nodes (communities) with high intra-group and low inter-group communications. This problem is NP-hard, and most existing algorithms are global with high computational complexity, especially for large networks. Recently, local methods with acceptable computational complexity have been developed, but many have low accuracy and are non-deterministic. This paper introduces a new local algorithm, LCD-SN, which identifies communities based on first- and second-degree neighbor nodes. Unlike other local algorithms, LCD-SN is highly accurate, definitive, and not dependent on initial seed nodes. Additionally, a new index is proposed to determine the importance of network nodes using their local characteristics (first- and second-degree neighbors). Using this index, LCD-SN first identifies important nodes, forms initial communities with these nodes and their first-degree neighbors, and then obtains final communities through post-processing. Experiments show that LCD-SN is effective in identifying communities in social networks.

关键词： Indexes Social networking (online) Clustering algorithms partitioning algorithms Accuracy Chaotic communication Time complexity Community structure detection modularity nodes ranking social network analysis

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End-to-End Multiview Fuzzy Clustering With Double Representation Learning and Visible-Hidden View Cooperation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第2期32卷 483-497页

作者： Yang, Hongtan Deng, Zhaohong Zhang, Wei Wu, Qunzhuo Choi, Kup-Sze Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

Multiview clustering has received great attention in recent years for the potential in clustering performance improvement by using cooperative learning of different views. Despite the considerable progress, a few issues remain: 1) real multiview data contains redundant features and noises that lead to unsatisfactory clustering performance;2) most existing multiview clustering methods only mine the shared information between views and ignore the specific information within views;and 3) most multiview clustering methods are based on a two-step framework that learn the hidden view representation and then perform clustering, overlooking the correlation between the two processes. Although some approaches have been proposed to deal with these issues, they cannot them simultaneously. To this end, we propose an end-to-end multiview fuzzy clustering. First, we construct a multiview fuzzy clustering framework to mine the specific information of the visible views. Second, to reduce the impact of redundant features and noises on clustering performance, we introduce the orthogonal projection matrix into the clustering framework to learn the low-dimensional representation of the visible views. Meanwhile, this procedure is integrated into the clustering framework. Third, we explore the shared hidden view representation between the visible views by multiview non-negative matrix factorization and integrate it into the clustering framework to realize visible-hidden view cooperation learning. Finally, the shared hidden view representation learning between visible views, the low-dimensional representation learning of visible views, and the clustering partition of multiview data negotiate with each other in the end-to-end learning framework. Extensive experiments on benchmark multiview datasets indicate the superiority of the proposed method over state-of-the-art methods.

关键词： Clustering methods Representation learning Clustering algorithms partitioning algorithms Linear programming Correlation Loss measurement Fuzzy clustering multiview learning visible-hidden cooperation learning representation learning

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Decentralized and Incremental Discovery of Relaxed Functional Dependencies Using Bitwise Similarity

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 7380-7398页

作者： Breve, Bernardo Caruccio, Loredana Cirillo, Stefano Deufemia, Vincenzo Polese, Giuseppe Univ Salerno Dept Comp Sci I-84084 Fisciano Italy

Over the past decade, there have been numerous extensions to the definition of Functional Dependency (fd), culminating in the introduction of Relaxed Functional Dependency (rfd), offering more flexible constraints compared to traditional fds. This increased flexibility makes rfds well-suited for exploring and profiling data in datasets with lower data quality. However, efficiently identifying rfds within dynamic data sources presents a significant challenge, as it requires processing an entire dataset from scratch whenever modifications occur. To tackle this problem, incremental discovery algorithms have been defined, but they often suffer when the frequency and the size of batches of updates increase. This article presents a new algorithm, namely D-IndiBits, relying on a new decentralized architecture to balance the workload that drives the incremental discovery process of IndiBits, which is based on bitwise operators for computing attribute similarities. Experiments demonstrate D-IndiBits's effectiveness compared to fd and rfd discovery algorithms on both static and dynamic real-world data. With batches of modifications of sizes 10 k and 100 k, D-IndiBits is capable of updating the set of rfds in a few seconds, whereas all other approaches often employ more than 3 hours.

关键词： Heuristic algorithms partitioning algorithms Metadata Vectors Lattices Task analysis Symbols Data profiling relaxed functional dependencies incremental scenarios bitwise similarities

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Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

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IEEE ACCESS 2024年 12卷 84763-84773页

作者： Zhao, Xiumei Zhong, Xincheng Han, Bing Changzhi Univ Dept Comp Changzhi 046000 Peoples R China

Traditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates. Thus, we propose a frequent closed high-utility itemset mining (FCHUIM) algorithm based on Leiden community detection and a compact genetic algorithm (LcGA). This algorithm first employs Leiden community detection to decompose a dataset into several highly related transaction communities and then uses an approximate or exact strategy to mine frequent itemsets. Subsequently, it checks for closures in the mined frequent itemsets and finally employs the compact genetic algorithm to efficiently mine high-utility patterns from the frequent closed itemsets. Experimental results on four real datasets, namely Retail, Chainstore, Chess, Accidents,among which the datatype of the former two is Sparse and that of the latter two is Dense, demonstrate that compared with modified traditional closed high-utility mining algorithms, including CHUI-Miner-S, CLS-Miner-S and the most advanced algorithms for mining frequent closed high-utility mining algorithms called FCHUIM, the average runtime of LcGA-FCHUIM is lower by 40.5% than the optimal contrastive algorithm. Moreover, LcGA-FCHUIM can mine an average of 96% of frequent closed high-utility itemsets, making it an effective algorithm for frequent closed high-utility itemset mining and suitable for most scenarios.

关键词： Itemsets Clustering algorithms Data mining Genetic algorithms partitioning algorithms Accuracy Upper bound Frequent closed high-utility itemset mining (FCHUIM) Leiden community detection compact genetic algorithm (cGA) approximate strategy exact strategy

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A Hardware-Friendly Lightweight Partition Decision Algorithm for VVC Intra and Inter Coding

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 941-945页

作者： Zan, Zhao Huang, Leilei Chen, Shushi Zeng, Xiaoyang Fan, Yibo Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 201203 Peoples R China East China Normal Univ Inst Microelect Circuits & Syst Shanghai 200241 Peoples R China

The Versatile Video Coding (VVC) standard notably enhances encoding efficiency with the Quad-Tree plus Multi-Type Tree (QTMTT) partition structure. However, the complex QTMTT tool presents substantial challenges in both software and hardware implementation. To overcome those challenges, this paper introduces a hardware-friendly partition decision algorithm for VVC intra and inter coding. Firstly, we propose a lightweight backbone network to extract partition-aware features. Secondly, we employ a Quantisation Parameter (QP) fusion network to regulate the impact of QPs on the partition structure. Additionally, we apply a top-down threshold-driven post-processing algorithm, in which improbable partition types are removed to directly derive the unique partition structure. Experiments show that our method not only exceeds the previous state-of-the-art work in BD-BR performance, but also shows sufficient hardware-friendly characteristics. To the best of our knowledge, this work is among the earliest to comprehensively discuss and implement a hardware-friendly partition decision algorithm.

关键词： partitioning algorithms Hardware Encoding Signal processing algorithms Prediction algorithms Feature extraction Complexity theory Software algorithms Convolution Software VVC QTMTT partition decision complexity reduction hardware-friendly algorithm convolutional neural network intra coding inter coding

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Structure-Attribute-Based Social Network Deanonymization With Spectral Graph partitioning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第3期9卷 902-913页

作者： Jiang, Honglu Yu, Jiguo Cheng, Xiuzhen Zhang, Cheng Gong, Bei Yu, Haotian George Washington Univ Dept Comp Sci Washington DC 20052 USA Univ Texas Rio Grande Valley Dept Comp Sci Brownsville TX 78520 USA Qilu Univ Technol Sch Comp Sci Shandong Acad Sci Jinan 250353 Peoples R China Shandong Lab Comp Networks Jinan 250014 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266510 Peoples R China West Texas A&M Univ Paul & Virginia Engler Coll Business Canyon TX 79016 USA Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China George Washington Univ Dept Data Analyt Washington DC 20052 USA

Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. However, the publishing of social network data raises more and more privacy concerns. To protect user privacy, social networking data are usually anonymized before being released. Nevertheless, existing anonymization techniques do not have sufficient protection effects. A large number of deanonymization attacks have arisen, and they mainly make use of either network topology or node attribute information to successfully reidentify anonymized users. In this article, we model a social network as a structure-attribute network (SAN) integrating the structural characteristics and the attribute information associated with social network users. A novel similarity measurement of social network nodes is proposed by considering the structural similarity and attribute similarity. A two-phase scheme is then designed to perform deanonymization by first dividing a social network (graph) into smaller subgraphs based on spectral graph partitioning and then applying the proposed deanonymization algorithm on each matched subgraph pair. We simulate the deanonymization attack with extensive experiments on three real-world datasets, and the experimental results demonstrate that our approach can improve the accuracy and time complexity of deanonymization compared with the state of the art.

关键词： Social networking (online) Electronic mail Solid modeling partitioning algorithms Computer science Measurement Network topology Deanonymization differential privacy k-anonymity social network

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Block-Diagonal Guided DBSCAN Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5709-5722页

作者： Xing, Zheng Zhao, Weibing Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Shenzhen 518115 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst Sch Sci & Engn Shenzhen 518172 Peoples R China

Cluster analysis constitutes a pivotal component of database mining, with DBSCAN being one of the most extensively employed algorithms in this domain. Nevertheless, DBSCAN is encumbered by several limitations, including challenges in processing high-dimensional datasets, a pronounced sensitivity to input parameters, and inconsistencies in generating reliable clustering outcomes. This paper presents a refined version of DBSCAN that utilizes the block-diagonal property of similarity graphs to enhance the clustering process. The core concept involves the construction of a graph that assesses the similarity among high-dimensional data points, capable of transformation into a block-diagonal form via an unknown permutation. This is followed by a cluster-ordering procedure that establishes the requisite permutation, thereby facilitating the straightforward identification of clustering structures through the recognition of diagonal blocks in the permuted graph. The principal obstacle addressed in this study is the construction of a graph that inherently possesses a block-diagonal structure, the permutation of this graph to actualize such a structure, and the autonomous identification of diagonal blocks within the permuted graph. To surmount these challenges, we initially devise a block-diagonal constrained self-representation model to create a similarity graph that exhibits a block-diagonal form post-permutation. A gradient descent-based methodology is proposed to resolve this problem effectively. Concurrently, we engineer a traversal algorithm, inspired by DBSCAN, that discerns clusters of high density within the graph and generates an enhanced cluster ordering. The attainment of a block-diagonal structure is then realized through permutation aligned with the traversal sequence, laying a robust foundation for both automated and interactive cluster analysis. Moreover, a novel split-and-refine algorithm is introduced to autonomously identify all diagonal blocks within the pe

关键词： Clustering algorithms Noise Indexes Clustering methods Shape partitioning algorithms Databases Cluster analysis DBSCAN block-diagonal property self-representation graph permutation diagonal block identification

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Concept Factorization Based Multiview Clustering for Large-Scale Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5784-5796页

作者： Chen, Man-Sheng Wang, Chang-Dong Huang, Dong Lai, Jian-Huang Yu, Philip S. Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Guangdong Prov Key Lab Intellectual Property & Big Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China Univ Illinois Chicago IL 60607 USA

Most existing large-scale multiview clustering algorithms attempt to capture data distribution in multiple views by selecting view-wise anchor representations beforehand with k-means, or by direct matrix factorization on the original observations. Despite impressive performance, few of them have paid attention to the semantic correlations between anchor bases and cluster centroids, or even the underlying relations between clusters and data samples. In view of this, we propose a Concept Factorization based Multiview Clustering for Large-scale Data (CFMC) method with nearly linear complexity. The anchor bases learning, coefficient expression with clear semantic cues and partitioning are integrated together in this unified model. Meanwhile, explicit connections among multiview data, anchor bases and clusters are modeled via coefficient representations with semantic meanings. A four-step alternate minimizing algorithm is designed to handle the optimization problem, which is proved to have linear time complexity w.r.t. the sample size. Extensive experiments conducted on several challenging large-scale datasets confirm the superiority of the method compared with the state-of-the-art methods.

关键词： Semantics Clustering algorithms Data models partitioning algorithms Correlation Tensors Representation learning Multiview clustering large-scale concept factorization semantic cues

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Demodulation Scheme Against Phase Noise Using an Ensemble Clustering Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 12938-12951页

作者： Li, Yu Zhang, Jiaheng Xie, Ning Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Most wireless systems involve time-varying multipath channels, where the negative effect of the phase noise cannot be ignored. The phase noise can be introduced by imperfect phase-locked loop circuitry, imperfect channel estimation, or both. This paper considers the demodulation problem of wireless systems with phase noise. We propose an ensemble clustering algorithm to address the limitations of the existing demodulation schemes. The proposed ensemble clustering algorithm is named as the Ensemble Clustering algorithm using the Matrix Factorization and Information Theory (MFIT-EC). The MFIT-EC algorithm improves the performance of existing ensemble clustering algorithms, which neglect different clustering effects of different base-clustering algorithms or different effects of different clusters of the same base-clustering algorithm. Based on the MFIT-EC algorithm, we design a demodulation scheme in a coherent wireless system with phase noise, referred to as the MFIT-EC demodulation scheme. Specifically, we first utilize several base-clustering algorithms to obtain different base-clustering results. Second, we use a weighted ensemble mechanism to allocate different weights to different base-clustering results, and calculate the certainty of the clusters of each base-clustering algorithm to obtain a better and more robust demodulation performance. We implement the proposed approach and conduct extensive performance comparisons through simulations, which show that the proposed approach has better performance than the prior approaches in terms of both clustering performance and demodulation performance.

关键词： Clustering algorithms Demodulation partitioning algorithms Wireless communication Phase noise Measurement Phase locked loops demodulation clustering ensemble clustering wireless communications

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