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Multiobjective Deep clustering and Its Applications in Single-cell RNA-seq Data

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第8期52卷 5016-5027页

作者： Wang, Yunhe Bian, Chuang Wong, Ka-Chun Li, Xiangtao Yang, Shengxiang Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Jilin Univ Sch Artificial Intelligence Changchun 130000 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC.

关键词： clustering algorithms Gene expression Optimization RNA Sequential analysis Feature extraction Sociology Evolutionary multiobjective deep clustering (EMDC) multiobjective optimization single-cell RNA-seq dataset

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NMFLRR: clustering scRNA-Seq Data by Integrating Nonnegative Matrix Factorization With Low Rank Representation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第3期26卷 1394-1405页

作者： Zhang, Wei Xue, Xiaoli Zheng, Xiaoying Fan, Zizhu East China Jiaotong Univ Sch Sci Nanchang 330013 Jiangxi Peoples R China Naval Univ Engn Operat Res & planning Dept Wuhan 430072 Hubei Peoples R China

Fast-developing single-cell technologies create unprecedented opportunities to reveal cell heterogeneity and diversity. Accurate classification of single cells is a critical prerequisite for recovering the mechanisms of heterogeneity. However, the scRNA-seq profiles we obtained at present have high dimensionality, sparsity, and noise, which pose challenges for existing clustering methods in grouping cells that belong to the same subpopulation based on transcriptomic profiles. Although many computational methods have been proposed developing novel and effective computational methods to accurately identify cell types remains a considerable challenge. We present a new computational framework to identify cell types by integrating low-rank representation (LRR) and nonnegative matrix factorization (NMF);this framework is named NMFLRR. The LRR captures the global properties of original data by using nuclear norms, and a locality constrained graph regularization term is introduced to characterize the data's local geometric information. The similarity matrix and low-dimensional features of data can be simultaneously obtained by applying the alternating direction method of multipliers (ADMM) algorithm to handle each variable alternatively in an iterative way. We finally obtained the predicted cell types by using a spectral algorithm based on the optimized similarity matrix. Nine real scRNA-seq datasets were used to test the performance of NMFLRR and fifteen other competitive methods, and the accuracy and robustness of the simulation results suggest the NMFLRR is a promising algorithm for the classification of single cells. The simulation code is freely available at: https://***/wzhangwhu/NMFLRR_code.

关键词： clustering algorithms Sparse matrices Prediction algorithms Optimization Data models Mathematical model clustering methods Single-cell RNA sequencing Cell type identification Nonnegative matrix factorization Low-rank representation Optimization

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A Systematic Review of Density Grid-Based clustering for Data Streams

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IEEE ACCESS 2022年 10卷 579-596页

作者： Tareq, Mustafa Sundararajan, Elankovan A. Harwood, Aaron Abu Bakar, Azuraliza Al Hikma Univ Coll Dept Comp Technol Engn Baghdad 10015 Iraq Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Software Technol & Management Bangi 43600 Selangor Malaysia Univ Melbourne Sch Comp & Informat Sci Melbourne Vic 3010 Australia Univ Kebangsaan Malaysia Ctr Articial Intelligence & Technol Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia

Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerable interest due to the increasing usage of evolving data streams. In particular, evolving data streams affect clustering because they introduce numerous challenges, such as time and memory limits and one-pass clustering. Furthermore, researchers need to be able to determine arbitrarily shaped clusters present in evolving data streams from applications. Due to these characteristics, conventional density grid-based clustering techniques cannot be used. Moreover, the existing density grid-based clustering algorithms have low cluster quality for clustering evolving data streams. This study conducted a systematic literature review (SLR) and noted numerous research-related issues encountered in solving the aforementioned problems. We summarized numerous grid-based clustering algorithms that have been used and determined their distinctive and limited features. We also observed how these algorithms address the challenges affecting the clustering of evolving data streams and studied their advantages and disadvantages. SLR was based on 104 articles published between 2010 and 2021. Numerous challenges remain for grid-based clustering algorithms, particularly in terms of time-limited and high-dimensional data handling. Last, our findings indicated a variety of active studies on density grid-based clustering.

关键词： clustering algorithms Data mining Systematics Protocols Databases Real-time systems Licenses clustering data stream grid-based clustering data stream clustering density-based clustering

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An Efficient Vertex-Driven Temporal Graph Model and Subgraph clustering Method

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IEEE ACCESS 2022年 10卷 100627-100645页

作者： Zhang, Hanlin Ding, Linlin Zhang, Gang Pan, Yishan Song, Baoyan Liaoning Univ Sch Informat Inst Shenyang 110036 Liaoning Peoples R China Liaoning Univ Sch Environm Inst Shenyang 110036 Liaoning Peoples R China

The temporal graph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporal graph would embody vertex updates frequently, high time resolution, and not enumerated rules. The construction and update of some temporal graph models are too dependent on the graph operation sequence, which leads to a lack of an effective model. Simultaneously, the temporal subgraph clustering of the temporal graph with frequent updating for the lack of an effective model leads to low accuracy. Therefore, we propose an efficient and frequently updated temporal graph model as vertex driven and corresponding temporal subgraph clustering method. First, we propose a temporal graph construction algorithm and set two thresholds to divide the temporal graph on a timeline to obtain temporal subgraphs. Next, an enhancement strategy based on the sliding window is proposed to accelerate the construction process. Third, we offer a double-standard temporal subgraph clustering method based on community comparison and temporal distance. The temporal subgraph can be effectively distinguished in temporal and structure dimensions. Lastly, experimental results on both real and synthetic datasets show that the temporal graph model proposed in this work can reduce the time overhead of construction compared to other existing models. The cluster method improves the clustering accuracy of temporal subgraphs. The clustering results show through the hierarchical clustering at the same time.

关键词： Sensors Analytical models Image edge detection clustering methods Data models clustering algorithms Graph modeling Temporal Graph temporal graph model subgraph clustering sliding window hierarchical clustering

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Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

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IEEE ACCESS 2024年 12卷 72117-72130页

作者： Wang, Haiying Tao, Jianyu Li, Hansheng Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Peoples R China Shaanxi Transportat Holding Grp Co Ltd Xian 710075 Peoples R China

The existing methods for pavement crack classification and identification solely offer information about the crack type, neglecting size and direction details, which are essential for guiding repair efforts and forming the engineer digital information data. In response to the challenges posed by insufficient crack information, prolonged training time and intricate parameter adjustment inherent in employing deep learning algorithms for pavement crack classification and recognition, we propose an integrated approach combining tensor voting with the random sample consensus for pavement crack classification and recognition. The method involves pre-processing road images using gray value transformation and the K-Means clustering algorithm. Subsequently, the tensor voting algorithm is applied to enhance the linear features, resulting in the generation of linear saliency maps of cracks along with crack junction information. Furthermore, a non-maximum suppression method and the RANSAC algorithm are employed to refine and fit the crack skeleton curves respectively, accomplishing the crack classification and recognition. The outcomes demonstrate that the proposed integrated approach in the crack skeleton segmentation algorithm yields an average F1-score of 0.7879, outperforming traditional non-maximum suppression methods. The accuracy of crack classification and recognition reaches 96%, outperforming other crack classification and recognition algorithms grounded in digital image processing methods. Compared with the neural networks employed for classification and recognition, the proposed algorithm is able to capture direction and size details of cracks, which can provide guidance for intelligent crack repair. This additional information can offer valuable guidance for intelligent crack repair processes.

关键词： Classification algorithms clustering algorithms Tensors Image segmentation Roads Interference Surface cracks Random sequences Crack classification and recognition non-maximum suppression random sample consensus (RANSAC) tensor voting

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Analysis of Chinese generic drug research hotspots and evolutionary trends based on clustering algorithm 24

Analysis of Chinese generic drug research hotspots and evolu...

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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Zhang, Ludan Sheng, Xinmeng Zhao, Ziwei Cao, Shikui School of Health Management Changchun University of Chinese Medicine Chang Chun China Graduate School Changchun University of Chinese Medicine Chang Chun China

ISBN: (纸本)9798400718106

China’s generic drug industry has made great progress in recent years and become an important player in the global generic drug market. In this study, CNKI was used as the data source, and clustering and Cosine similarity algorithms were invoked to explore the development trend and research hotspots in the field of generic drugs in China, and the authors, issuing organizations, keywords, etc. of the screened eligible literature were analyzed for co-occurrence, and the knowledge map was drawn, and a total of 1,749 articles were included in the screened literature. The results show that the annual publication number of literature overall fluctuating growth trend. The authors of the publications mainly include Xu Mingyi, Shao Rong, and Niu Jianzhao, and the institutions are mainly China Academy of Food and Drug Control and China Pharmaceutical University. The research hotspots mainly focus on generic drugs, original drugs, drug registration, etc., of which band purchasing is the focus of generic drug research in recent years. The overall development of domestic generic drug research is good, but the influence of the published papers is insufficient. The cooperation between authors is relatively close, but the cross-institutional research cooperation needs to be strengthened. © 2024 ACM.

关键词： clustering algorithms

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Multi-Criterion Partial clustering Algorithm for Wireless Sensor Networks

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IEEE ACCESS 2022年 10卷 108366-108373页

作者： Neamatollahi, Peyman Hakim Sabzevari Univ Fac Comp & Elect Engn Comp Engn Dept Sabzevar *** Iran

Cluster architectures are an effective approach for organizing sensor networks to balance the load and prolong network life. To cluster wireless sensor networks, this paper proposes an energy-efficient distributed algorithm. This algorithm uses two techniques (partial clustering and multi-criterion cluster formation) for efficient use of the sensor nodes' energy. When a header expends a certain amount of power, it only notifies the nodes in its cluster that new clustering is required in the next round. Therefore, in contrast to previous studies that performed complete clustering, clustering in the present work is performed partially, which considerably reduces the clustering overhead. In addition, a multi-criterion score is calculated for each node. In this algorithm, a node with the highest remaining energy and score is a more suitable candidate to be selected as the head of the cluster. In addition, a regular node becomes the member of the cluster with the highest score in its vicinity. The experiments reveal the superiority of the proposed algorithm over other simulated algorithms in terms of energy savings and network lifetime.

关键词： clustering algorithms Wireless sensor networks Heuristic algorithms Time division multiple access Steady-state Energy efficiency Spread spectrum communication Distributed processing Wireless sensor networks Load management Sensor networks clustering network lifetime energy efficiency distributed algorithm

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Improved Normalized Cut for Multi-View clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第12期44卷 10244-10251页

作者： Zhong, Guo Pun, Chi-Man Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China

Spectral clustering (SC) algorithms have been successful in discovering meaningful patterns since they can group arbitrarily shaped data structures. Traditional SC approaches typically consist of two sequential stages, i.e., performing spectral decomposition of an affinity matrix and then rounding the relaxed continuous clustering result into a binary indicator matrix. However, such a two-stage process could make the obtained binary indicator matrix severely deviate from the ground true one. This is because the former step is not devoted to achieving an optimal clustering result. To alleviate this issue, this paper presents a general joint framework to simultaneously learn the optimal continuous and binary indicator matrices for multi-view clustering, which also has the ability to tackle the conventional single-view case. Specially, we provide theoretical proof for the proposed method. Furthermore, an effective alternate updating algorithm is developed to optimize the corresponding complex objective. A number of empirical results on different benchmark datasets demonstrate that the proposed method outperforms several state-of-the-arts in terms of six clustering metrics.

关键词： Laplace equations clustering algorithms Optimization clustering methods Matrix decomposition Linear programming Fuses clustering multi-view data normalized cut

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Index-Based Solutions for Efficient Density Peak clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第5期34卷 2212-2226页

作者： Rasool, Zafaryab Zhou, Rui Chen, Lu Liu, Chengfei Xu, Jiajie Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China

Density Peak clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter d(c), which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index.

关键词： Indexes clustering algorithms Software algorithms Approximation algorithms Histograms Memory management Computer science clustering density peak index efficiency algorithms

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Fuzzy K-Means clustering With Discriminative Embedding

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1221-1230页

作者： Nie, Feiping Zhao, Xiaowei Wang, Rong Li, Xuelong Li, Zhihui Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710072 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function. In these methods, the fuzzy membership degree matrix is obtained based on the calculation of the distance between data points in the original space. However, this operation may lead to suboptimal results because of the influence of noises and redundant features. Besides, some FKM clustering methods ignore the importance of the weighting exponent. In this paper, we propose a novel FKM method called Fuzzy K-Means clustering With Discriminative Embedding. Within this method, we simultaneously conduct dimensionality reduction along with fuzzy membership degree learning. To retain most information in the embedding subspace and improve the robustness of this method, principal component analysis is incorporated into our framework. An iterative optimization algorithm is proposed to solve the model. To validate the efficacy of the proposed method, we perform comprehensive analyses, including convergence behavior, parameter determination and computational complexity. Moreover, we also match a appropriate weighting exponent for each data set. Experimental results on benchmark data sets show that the proposed method is more discriminative and effective for clustering tasks.

关键词： clustering algorithms Linear programming clustering methods Principal component analysis Noise measurement Dimensionality reduction Task analysis Fuzzy K-Means dimensionality reduction most information principal component analysis

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