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Managing Knowledge Requirements in the NPD Portfolio Selection Process Using Knowledge clustering and Prioritization: A Case Study of an Australian Train Operator

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 2973-2985页

作者： Dastaki, Mohsen Sadeghi Mahootchi, Masoud Moehler, Robert Afrazeh, Abbas Amirkahir Univ Technol Dept Ind Engn & Management Syst Tehran 158754413 Iran Monash Univ Dept Civil Engn Clayton Vic 3800 Australia

In recent decades, knowledge-oriented new product development (NPD) has turned into a significant competitive advantage. It largely depends on the proper management of knowledge processes performed by knowledge management (KM). Among different KM processes addressed in the KM literature, knowledge clustering and prioritization (KCP) has received scant attention. However, it has a significant impact on the performance of other KM processes and is an integral part of the NPD process. KCP helps KM and NPD managers identify knowledge areas' importance, risk, and cost implications in knowledge-intensive industries such as machinery and appliances. This article addresses this issue by introducing a two-phase research approach to cluster and prioritize knowledge areas. In the first phase, the study data, including knowledge areas, characteristics, and comparisons, are collected through an interview. Then, in the second phase, best-worst method (BWM) and K-means algorithm are employed to prioritize and cluster knowledge areas, respectively. The approach is applied to prioritize and cluster ten knowledge areas adopted by the operations team of an Australian train operator. Results revealed that site and construction managements are vital knowledge areas, whereas change management, programming, and cost control are less urgent for the operation teams.

关键词： Companies Costs Decision making Portfolios Production clustering algorithms Product development Best-worst method (BWM) k-means algorithm knowledge clustering knowledge prioritization new product development (NPD) process

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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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Heterogeneous density-based clustering with a dual-functional memristive array

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MATERIALS HORIZONS 2024年第18期11卷 4493-4506页

作者： Shin, Dong Hoon Cheong, Sunwoo Lee, Soo Hyung Jang, Yoon Ho Park, Taegyun Han, Janguk Shim, Sung Keun Kim, Yeong Rok Han, Joon-Kyu Baek, In Kyung Ghenzi, Nestor Hwang, Cheol Seong Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea Seoul Natl Univ Coll Engn Inter Univ Semicond Res Ctr Seoul 08826 South Korea Univ Avellaneda UNDAV Mario Bravo 1460 RA-1872 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Conicet Mario Bravo 1460 RA-1460 Buenos Aires DF Argentina Sogang Univ Syst Semicond Engn & Dept Elect Engn Seoul South Korea

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems. A reconfigurable dual-mode memristive array was utilized for the proposed heterogeneous density-based clustering. Both digital and analog modes were analyzed, and the algorithm was verified through vector-matrix multiplication in both modes.

关键词： clustering algorithms

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Incomplete Multiple Kernel Alignment Maximization for clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第3期46卷 1412-1424页

作者： Liu, Xinwang Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Multiple kernel alignment (MKA) maximization criterion has been widely applied into multiple kernel clustering (MKC) and many variants have been recently developed. Though demonstrating superior clustering performance in various applications, it is observed that none of them can effectively handle incomplete MKC, where parts or all of the pre-specified base kernel matrices are incomplete. To address this issue, we propose to integrate the imputation of incomplete kernel matrices and MKA maximization for clustering into a unified learning framework. The clustering of MKA maximization guides the imputation of incomplete kernel elements, and the completed kernel matrices are in turn combined to conduct the subsequent MKC. These two procedures are alternately performed until convergence. By this way, the imputation and MKC processes are seamlessly connected, with the aim to achieve better clustering performance. Besides theoretically analyzing the clustering generalization error bound, we empirically evaluate the clustering performance on several multiple kernel learning (MKL) benchmark datasets, and the results indicate the superiority of our algorithm over existing state-of-the-art counterparts.

关键词： Kernel clustering algorithms Optimization Task analysis Pattern analysis Partitioning algorithms Minimization Multiple kernel clustering multi-view clustering kernel alignment maximization

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clustering-Based Downlink Scheduling of IRS-Assisted Communications With Reconfiguration Constraints

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18487-18501页

作者： Rech, Alberto Pagin, Matteo Badia, Leonardo Tomasin, Stefano Giordani, Marco Gambini, Jonathan Zorzi, Michele Univ Padua Dept Informat Engn I-35131 Padua Italy Huawei Paris Res Ctr F-92100 Boulogne Billancourt France Huawei Milan Res Ctr I-20147 Milan Italy

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost;thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.

关键词： Downlink Wireless communication Time division multiple access clustering algorithms Millimeter wave communication Optimization NOMA Costs Base stations Terahertz communications Intelligent reflecting surfaces (IRS) millimeter wave (mmWave) communication multiple access scheduling clustering optimization

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Sparse Membership Affinity Lasso for Fuzzy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1553-1563页

作者： Huang, Junjun Lu, Xiliang Yang, Jerry Zhijian Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China YGSoft Inc Zhuhai 519085 Peoples R China Wuhan Univ Hubei Key Lab Computat Sci Wuhan 430072 Peoples R China

The membership matrix is a key element in fuzzy clustering, enabling novel data representation in multiple clusters. The row vectors of the membership matrix represent each sample's degree of membership to different clusters. Notably, researchers have confirmed the presence of the local affinity among these row vectors, effectively preserving the local structure of the original data distribution. However, in this work, we consider that most sample points have insignificant fuzziness, with fuzziness found primarily in a few clusters, resulting in most membership vectors being sparse. To tackle this issue, we present the sparse, membership-affinity fuzzy clustering model, which leverages the sparsity of the row vectors and its affinity to establish a more appropriate representation, along with an optimization algorithm. Our experimental results on both simulated and real datasets demonstrate that the combination of sparsity and affinity can significantly enhance fuzzy clustering performance over other models.

关键词： clustering algorithms Entropy Geometry Encoding Sparse matrices Robustness Linear programming Fuzzy clustering membership affinity lasso membership sparsity

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SA-PSO-GK plus plus : A New Hybrid clustering Approach for Analyzing Medical Data

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IEEE ACCESS 2024年 12卷 12501-12516页

作者： Abdo, Amani Abdelkader, Omnia Abdel-Hamid, Laila Arab Open Univ Fac Comp Cairo 11211 Egypt Helwan Univ HU Fac Comp & Artificial Intelligence Cairo 11795 Egypt

Data clustering is an unsupervised learning task that has been extensively studied, given its wide applicability in various domains. Traditional algorithms often struggle to achieve a balance between exploration and exploitation, leading to sub-optimal solutions. This paper presents a novel hybrid algorithm named SA-PSO-GK++ that synergistically combines Particle Swarm Optimization (PSO), K-means++, Simulated Annealing (SA), and Gaussian Estimation of Distribution to tackle this issue effectively. The proposed SA-PSO-GK++ aims to overcome the drawbacks of existing methods by leveraging the strengths of each individual algorithm. The K-means++ initialization reduces the risk of poor initial centroids, while PSO aids in efficient search space exploration. GED provides a statistical model of the particle space, enabling the algorithm to generate new potential solutions that are statistically guided by the current best solutions. Additionally, the incorporation of Simulated Annealing allows the algorithm to escape local minima, thereby enhancing its global search capability. We evaluate the effectiveness of SA-PSO-GK++ using benchmark datasets from the UCI Machine Learning Repository, including the Iris, Breast cancer, Heart datasets and contraceptive method choice datasets. The proposed method outperforms conventional and some of the state-of-the-art hybrid clustering algorithms in terms of sum of euclidean distance, normalized index, and error rates. These advantages make SA-PSO-GK++ a compelling option for a wide range of clustering applications. The results offer promising avenues for future research in optimizing and applying this innovative clustering technique in diverse domains.

关键词： clustering algorithms Particle swarm optimization Convergence Estimation Simulated annealing Optimization Robustness Swarm intelligence particle swarm optimization (PSO) K-means K-means plus plus simulated annealing Gaussian estimation data clustering big data cluster convergence clustering metrics local optima

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One-Step Multiview Fuzzy clustering With Collaborative Learning Between Common and Specific Hidden Space Information

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 14031-14044页

作者： Zhang, Wei Deng, Zhaohong Zhang, Te Choi, Kup-Sze Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jiangsu Key Lab Digital Design & Software Technol Wuxi 214122 Peoples R China Key Lab Computat Neurosci & Brain Inspired Intelli Shanghai 200433 Peoples R China ZJLab Shanghai 200433 Peoples R China Univ Nottingham Sch Comp Sci Lab Uncertainty Data & Decis Making LUCID Nottingham NG8 1BB England Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

关键词： clustering methods Learning systems clustering algorithms Data mining Collaboration Feature extraction Partitioning algorithms Common space fuzzy clustering multiview data one-step clustering specific space

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Automatically Detecting Anchor Cells and clustering for scRNA-Seq Data Using scTSNN

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第11期28卷 7015-7027页

作者： Liu, Qiaoming Zhang, Dandan Wang, Dong Wang, Guohua Wang, Yadong Harbin Inst Technol Sch Med & Hlth Zhengzhou 150001 Peoples R China Harbin Inst Technol Zhengzhou Res Inst Zhengzhou 150001 Peoples R China Harbin Med Univ Dept Obstet & Gynecol Affiliated Hosp 1 Harbin 150001 Peoples R China North China Elect Power Univ Dept Comp Baoding 071003 Peoples R China Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.

关键词： Tensors Mathematical models Synthetic data clustering algorithms Noise Accuracy Spatial databases clustering single-cell RNA-seq anchor graph tensor graph cell heterogeneity

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Deep Fuzzy clustering Network With Matrix Norm Regularization

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IEEE ACCESS 2024年 12卷 28677-28683页

作者： Chen, Feiyu Li, Yan Wang, Wei Chongqing Normal Univ Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Army Med Univ Dept Comp Sci Chongqing 400038 Peoples R China

Recently, deep clustering networks, which able to learn latent embedding and clustering assignment simultaneously, attract lots of attention. Among the deep clustering networks, the suitable regularization term is not only beneficial to training of neural network, but also enhancing clustering performance. In the paper, we propose a deep fuzzy clustering network with mixed matrix norm regularization (DFCNR). Specifically, DFCNR uses the weighted intra-class variance as clustering loss, l(1,2) norm and the Frobenius norm of soft assignment matrix as regularization term, where the minimization of l(1,2) norm aims to achieve balanced assignment, and maximization of Frobenius norm aims to achieve discriminative assignment. Moreover, by solving the quadratic convex constraint optimization problem about soft assignment, we derive the activation function of clustering layer. Extensive experiments conducted on several datasets illustrate the superiority of the proposed approach in comparison with current methods.

关键词： clustering methods Training Representation learning clustering algorithms Linear programming Vectors Neural networks Encoding Deep learning Fuzzy systems Autoencoder deep fuzzy clustering deep learning matrix norm regularization

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