4.5 Review

Semi-supervised and un-supervised clustering: A review and experimental evaluation

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Semi-Supervised Clustering Under a Compact-Cluster Assumption

Zhen Jiang et al.

Summary: Semi-supervised clustering aims to utilize prior knowledge to improve clustering performance. Existing methods do not adequately consider the natural gap between class information and clustering when using partial labeling information. In order to address this issue, a compact-cluster assumption is proposed along with a general framework called CSSC, which supervises traditional clustering using an objective function that measures the compactness of clusters. Two effective solutions for Kmeans and spectral clustering are provided within this framework. The proposed method is shown to be feasible and effective through theoretical analyses and extensive experiments on real-world datasets.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Article Computer Science, Artificial Intelligence

Graph Transfer Learning via Adversarial Domain Adaptation With Graph Convolution

Quanyu Dai et al.

Summary: This paper addresses the issue of cross-network node classification by leveraging label information from a partially labeled source network to assist classification in a completely unlabeled or partially labeled target network. Existing single network learning methods and some multi-network learning methods are unable to solve this problem due to domain shift and reliance on cross-network connections. To overcome these limitations, the authors propose AdaGCN, a novel graph transfer learning framework that combines adversarial domain adaptation and graph convolution techniques. Empirical evaluations on real-world datasets demonstrate the effectiveness of AdaGCN in transferring class information despite low label rates and substantial distribution divergence between the source and target domains.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Review Computer Science, Artificial Intelligence

Graph-Based Semi-Supervised Learning: A Comprehensive Review

Zixing Song et al.

Summary: This article introduces a graph-based semi-supervised learning (GSSL) method, which represents each sample as a node in a graph and infers the label information of unlabeled samples based on the graph's structure. The article provides an in-depth understanding of GSSL methods and their advancements, as well as insights into future research directions in this field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results

Alexander Mey et al.

Summary: This survey explores semi-supervised learning and its applications in classification and regression tasks. It summarizes theoretical results and highlights the assumptions made when utilizing unlabeled data. The survey aims to identify the limits and potential benefits of semi-supervised learning, focusing on understanding the underlying theory and assumptions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

Article Computer Science, Information Systems

Fine-Grained Adversarial Semi-Supervised Learning

Daniele Mugnai et al.

Summary: This article explores the use of Semi-Supervised Learning (SSL) to improve Fine-Grained Visual Categorization (FGVC) by increasing the amount of training data. The proposed approach combines unlabeled data with adversarial optimization and a second-order pooling model to back-propagate information onto unlabeled data. Experimental results on multiple datasets demonstrate the effectiveness of this approach and its superior performance compared to previous methods and supervised learning.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Variable-Length Subsequence Clustering in Time Series

Jiangyong Duan et al.

Summary: This paper proposes an optimization framework for adaptively estimating the lengths and representations of different patterns in subsequence clustering. By minimizing the errors in subsequence clustering and segmentation under time series cover constraint, our framework can automatically extract unknown variable-length subsequence clusters in time series.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Information Systems

Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems

Chengqiang Huang et al.

Summary: This paper investigates the Support Vector Data Description (SVDD) method for detecting anomalous performance metrics of cloud services. It proposes a relaxed form of linear programming SVDD (RLPSVDD) and presents important insights into parameter selection for practical time series anomaly detection. Experiments validate the effectiveness of RLPSVDD in comparison to other methods.

IEEE TRANSACTIONS ON BIG DATA (2022)

Article Computer Science, Artificial Intelligence

SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks

Xiang Li et al.

Summary: This paper investigates the problem of clustering objects in an Attribute Heterogeneous Information Network (AHIN), considering both object attribute values and their structural connectedness in the network. It proposes the SCHAIN algorithm and two efficient variants, SCHAIN-PI and SCHAIN-IRAM. Experimental results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and efficiency.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

Time-Series Forecasting via Fuzzy-Probabilistic Approach With Evolving Clustering-Based Granulation

Weina Wang et al.

Summary: This article proposes a fuzzy-probabilistic prediction approach with evolving clustering-based granulation, which can accurately and efficiently achieve long-term prediction. The experimental results demonstrate its better performance for regular and Big Data time series.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2022)

Article Engineering, Electrical & Electronic

Reconfigurable Activation Functions in Integrated Optical Neural Networks

Jose Roberto Rausell Campo et al.

Summary: This article discusses the implementation of nonlinear activation functions in optical neural networks and compares the response of different electro-optic architectures. The study demonstrates that ring assisted MZI and two-ring assisted MZI have the highest expressivity among the proposed structures. The article also presents a quantitative analysis of the capabilities of optical devices to mimic state-of-the-art activation functions, and benchmarks the obtained activation functions on two machine learning examples.

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS (2022)

Article Engineering, Civil

Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation

Jianling Huang et al.

Summary: Glance behavior is crucial for driving safety, and this study aims to improve its accuracy and achieve spatiotemporal representation and visualization. By analyzing gaze points through statistical analysis and clustering methods during freeway driving simulation tasks, SSKM and CCPAR were found to be feasible for glance behavior modeling, providing higher accuracy and intuitive visualization.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Proceedings Paper Computer Science, Interdisciplinary Applications

Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation

Sophie Steger et al.

Summary: This paper connects the problem of semi-supervised clustering to constrained Markov aggregation, by considering data points as elements of a Markov chain's state space, defining transition probabilities between states based on similarities, and incorporating semi-supervision information as hard constraints in an algorithm. The introduced Constrained Markov Clustering (CoMaC) extends a recent information-theoretic framework for unsupervised clustering to the semi-supervised case, and further generalizes previous objectives for unsupervised clustering. Results indicate that CoMaC is competitive with state-of-the-art methods.

37TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (2022)

Article Computer Science, Artificial Intelligence

Design of Reinforced Hybrid Fuzzy Rule-Based Neural Networks Driven to Inhomogeneous Neurons and Tournament Selection

Congcong Zhang et al.

Summary: This article introduces a novel reinforced hybrid fuzzy rule-based neural networks (RHFNNs) by combining different types of neurons to enhance predictive abilities. Experimental results show that RHFNN achieves the best prediction accuracy on multiple machine learning datasets, indicating its superior performance.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2021)

Article Automation & Control Systems

Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

Dong Huang et al.

Summary: Ensemble clustering based on fast propagation of cluster-wise similarities via random walks addresses the issues of lack of information at higher levels of granularity and neglect of multiscale relationships in current ensemble clustering research. By constructing a cluster similarity graph and conducting random walks, a new cluster-wise similarity matrix is derived to achieve an enhanced co-association matrix. The proposed approach demonstrates effectiveness and efficiency through extensive experiments on various real-world datasets.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Incremental Factorization of Big Time Series Data with Blind Factor Approximation

Dan Chen et al.

Summary: This study proposes an incrementally parallel factorization solution for extracting latent factors of big time series data, which reveals key insights to the overall mechanisms. Through a phased algorithm and GPU cluster, this solution is capable of deriving multi-mode factors of augmenting big data without the need for prior knowledge.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Automation & Control Systems

An Elite Gene Guided Reproduction Operator for Many-Objective Optimization

Qingling Zhu et al.

Summary: Traditional reproduction operators in many-objective evolutionary algorithms may not be effective for tackling many-objective optimization problems due to large distances between parents in high-dimensional objective space. An elite gene-guided (EGG) reproduction operator is proposed to address this issue by building an elite gene pool from knee points in the population, exchanging genes with elite pool, and disturbing other genes under certain rates. Experimental analysis of exchange and disturbance rates is conducted, showing the effectiveness of the EGG operator in improving evolutionary algorithms for MaOPs.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Automation & Control Systems

Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach

Avisek Gupta et al.

Summary: This paper introduces a fuzzy clustering method called entropy c-means (ECM), which creates fuzzy clusters with different levels of fuzziness to accommodate clusters with varying degrees of overlap. Experimental results demonstrate that ECM outperforms traditional fuzzy clustering methods and previous multiobjective methods in cluster detection.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Computer Science, Artificial Intelligence

Clustering With Outlier Removal

Hongfu Liu et al.

Summary: Cluster analysis and outlier detection are closely related topics in data mining. The COR algorithm proposed in this article transforms the original space into a binary space through basic partitions and uses Holoentropy to measure cluster compactness. By introducing an auxiliary binary matrix, COR efficiently solves the joint cluster analysis and outlier detection problem through a unified K-means algorithm.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Engineering, Electrical & Electronic

Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids

Maryam Farajzadeh-Zanjani et al.

Summary: This paper introduces a novel adversarial scheme for semi-supervised learning to address the issues of partially labelled samples and skewed class distributions. By leveraging the generator and discriminator in an adversarial manner, more synthetic minority class samples are generated to train the model to learn the distribution of minority class samples, resulting in superior performance in diagnosing attacks and faults.

IEEE TRANSACTIONS ON SMART GRID (2021)

Article Automation & Control Systems

A Fast Semi-Supervised Clustering Framework for Large-Scale Time Series Data

Guoliang He et al.

Summary: The limitations of semi-supervised clustering algorithms include high computational complexity, underutilization of constraints, and difficulty in handling high-dimensional data, especially time series data. To efficiently cluster large-scale time series data, a semi-supervised time series clustering framework can be used, integrating fast similarity measure and constraint propagation methods to design effective algorithms.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021)

Proceedings Paper Computer Science, Hardware & Architecture

Clustering Ensemble via Cluster-wise Optimization Graph Learning

Huan Zhang et al.

Summary: This paper proposes a clustering ensemble method based on cluster level fusion, which measures reliability with different weights, ensures partition with block diagonal property, and obtains low-dimensional non-negative clustering label matrix using non-negative matrix factorization. Experiments show that this method outperforms several state-of-the-art baseline methods on different data sets.

IEEE INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN SYSTEMS SCIENCE AND ENGINEERING (IEEE RASSE 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning

Meng Liu et al.

Summary: This paper proposes a new diffusion-based hypergraph clustering algorithm that solves the problem of local hypergraph clustering, with high scalability and flexibility. It can not only handle various cardinality-based hypergraph cut functions, but also find clusters that satisfy a Cheeger-like quality guarantee.

PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Semi-Supervised Deep Learning for Multiplex Networks

Anasua Mitra et al.

Summary: In this study, a novel semi-supervised approach for structure-aware representation learning on multiplex networks is introduced, which maximizes the mutual information between local node-wise patch representations and label correlated global graph representations. Empirical results show that this approach outperforms state-of-the-art methods in various tasks, including classification, clustering, visualization, and similarity search across different experiment settings on seven real-world multiplex networks.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Article Computer Science, Artificial Intelligence

TEST: Triplet Ensemble Student-Teacher Model for Unsupervised Person Re-Identification

Yaoyu Li et al.

Summary: A novel Triplet Ensemble Student-Teacher (TEST) model is proposed for unsupervised person re-identification, which enhances descriptive ability through a collaborative learning mechanism between a teacher network and two student networks.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Penalized K-Means Algorithms for Finding the Number of Clusters

Behzad Kamgar-Parsi et al.

Summary: This study explores methods for determining the number of clusters in a dataset, proposing an approach to estimate the coefficient of the penalty term and deriving rigorous bounds for the additive penalty in k-means algorithm for ideal clusters. Experimental results suggest that using a multiplicative penalty produces more reliable results compared to an additive penalty under the assumption of ideal clusters.

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2021)

Article Computer Science, Information Systems

A Survey on Semi-, Self- and Unsupervised Learning for Image Classification

Lars Schmarje et al.

Summary: Current deep learning strategies in computer vision are highly dependent on labeled data, which may not be feasible for many real-world problems. Therefore, incorporating unlabeled data and addressing issues like class imbalance and robustness is crucial. Future research trends include scalability, decreasing supervision needs, and combining ideas from different methods for improved performance.

IEEE ACCESS (2021)

Article Computer Science, Information Systems

An Imbalanced Fault Diagnosis Method for Rolling Bearing Based on Semi-Supervised Conditional Generative Adversarial Network With Spectral Normalization

Minqiu Xu et al.

Summary: The study proposes a method using a Semi-supervised Conditional Generative Adversarial Network to address data imbalance in rolling bearings. By generating new samples with similar distribution, the dataset is effectively balanced, and satisfactory results are achieved in bearing fault diagnosis.

IEEE ACCESS (2021)

Article Computer Science, Artificial Intelligence

A survey on semi-supervised learning

Jesper E. Van Engelen et al.

MACHINE LEARNING (2020)

Article Computer Science, Artificial Intelligence

Reinforced Fuzzy Clustering-Based Ensemble Neural Networks

Eun-Hu Kim et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Adversarial Action Prediction Networks

Yu Kong et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition

Zhengming Li et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Ultra-Scalable Spectral Clustering and Ensemble Clustering

Dong Huang et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Article Automation & Control Systems

A Consensus Community-Based Particle Swarm Optimization for Dynamic Community Detection

Xiangxiang Zeng et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

Article Computer Science, Artificial Intelligence

Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters

Imran Khan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Proceedings Paper Computer Science, Information Systems

Product Bundle Identification using Semi-Supervised Learning

Hen Tzaban et al.

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) (2020)

Proceedings Paper Computer Science, Information Systems

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

Bingbing Xu et al.

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) (2020)

Proceedings Paper Computer Science, Software Engineering

Clustering Centroid Selection using a K-means and Rapid Density Peak Search Fusion Algorithm

Chenyang Zhang et al.

PROCEEDINGS OF 2020 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2020) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Learning from Incomplete Labeled Data via Adversarial Data Generation

Wentao Wang et al.

20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Ensemble Learning for Spectral Clustering

Hongmin Li et al.

20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) (2020)

Article Biochemical Research Methods

Ensembling of Gene Clusters Utilizing Deep Learning and Protein-Protein Interaction Information

Pratik Dutta et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)

Proceedings Paper Computer Science, Theory & Methods

Improving K-Mean Method by Finding Initial Centroid Points

Andleeb Aslam et al.

2020 22ND INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): DIGITAL SECURITY GLOBAL AGENDA FOR SAFE SOCIETY! (2020)

Article Computer Science, Information Systems

MEGA: Multi-View Semi-Supervised Clustering of Hypergraphs

Joyce Jiyoung Whang et al.

PROCEEDINGS OF THE VLDB ENDOWMENT (2020)

Article Computer Science, Information Systems

GRACE: A Graph-Based Cluster Ensemble Approach for Single-Cell RNA-Seq Data Clustering

Jihong Guan et al.

IEEE ACCESS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

TextCSN: a Semi-Supervised Approach for Text Clustering Using Pairwise Constraints and Convolutional Siamese Network

Lucas Akayama Vilhagra et al.

PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20) (2020)

Proceedings Paper Computer Science, Theory & Methods

Recurrent Attention Walk for Semi-supervised Classification

Uchenna Akujuobi et al.

PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20) (2020)

Article Computer Science, Information Systems

Unsupervised K-Means Clustering Algorithm

Kristina P. Sinaga et al.

IEEE ACCESS (2020)

Article Computer Science, Cybernetics

Sequential Cluster Estimation: A Generalized Model for Finding Large Numbers of Clusters in Data

Thomas A. Runkler

IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE (2020)

Article Computer Science, Information Systems

Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

Hui Yang et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Methods That Optimize Multi-Objective Problems: A Survey and Experimental Evaluation

Kamal Taha

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Adaptive Regularized Semi-Supervised Clustering Ensemble

Rui Luo et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

Qingbin Ji et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

Xin Bi et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Jie Liang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

Article Acoustics

General Sequence Teacher-Student Learning

Jeremy Heng Meng Wong et al.

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING (2019)

Article Engineering, Electrical & Electronic

Constrained Distance-Based Clustering for Satellite Image Time-Series

Thomas Lampert et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2019)

Article Computer Science, Information Systems

A Novel Algorithm for Initial Cluster Center Selection

Yating Li et al.

IEEE ACCESS (2019)

Article Computer Science, Theory & Methods

Context-aware rule learning from smartphone data: survey, challenges and future directions

Iqbal H. Sarker

JOURNAL OF BIG DATA (2019)

Article Computer Science, Information Systems

Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization

Jingtao Hu et al.

IEEE ACCESS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Incremental Class Discovery for Semantic Segmentation with RGBD Sensing

Yoshikatsu Nakajima et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Adversarial Variational Embedding for Robust Semi-supervised Learning

Xiang Zhang et al.

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)

Proceedings Paper Computer Science, Theory & Methods

Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding

Hansheng Xue et al.

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) (2019)

Proceedings Paper Computer Science, Theory & Methods

Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users

SeongKu Kang et al.

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) (2019)

Proceedings Paper Computer Science, Theory & Methods

SeiSMo: Semi-supervised Time Series Motif Discovery for Seismic Signal Detection

M. Ashraf Siddiquee et al.

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) (2019)

Proceedings Paper Computer Science, Interdisciplinary Applications

Imbalance-aware Pairwise Constraint Propagation

Hui Liu et al.

PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) (2019)

Article Computer Science, Information Systems

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Amir Ahmad et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm

Yonghao Gu et al.

IEEE ACCESS (2019)

Article Geochemistry & Geophysics

Morphological Band Selection for Hyperspectral Imagery

Jingyu Wang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

Article Engineering, Electrical & Electronic

Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning

Zilin Zhang et al.

IEEE SIGNAL PROCESSING LETTERS (2018)

Article Computer Science, Artificial Intelligence

On the Convergence of the Sparse Possibilistic C-Means Algorithm

Konstantinos D. Koutroumbas et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2018)

Article Computer Science, Information Systems

DOE-AND-SCA: A Novel SCA Based on DNN With Optimal Eigenvectors and Automatic Cluster Number Determination

Jinyin Chen et al.

IEEE ACCESS (2018)

Article Computer Science, Information Systems

A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture

Erxue Min et al.

IEEE ACCESS (2018)

Article Multidisciplinary Sciences

Multiresolution Consensus Clustering in Networks

Lucas G. S. Jeub et al.

SCIENTIFIC REPORTS (2018)

Article Computer Science, Artificial Intelligence

Multi-view Clustering: A Survey

Yan Yang et al.

BIG DATA MINING AND ANALYTICS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Automatic Estimation of Cluster Number in Fuzzy Co-clustering Based on Competition and Elimination of Clusters

Seiki Ubukata et al.

2018 JOINT 10TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 19TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS) (2018)

Proceedings Paper Computer Science, Software Engineering

Multi-objective Clustering Ensemble for Varying Number of Clusters

Sujoy Chatterjee et al.

2018 14TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS (SITIS) (2018)

Proceedings Paper Computer Science, Information Systems

Determining the Best Clustering Number of K-means Based on Bootstrap Sampling

Lianmin Yu et al.

2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND BUSINESS ANALYTICS (ICDSBA 2018) (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Prediction model of hot rolled strip quality based on K-means clustering and neural network

Xia Li et al.

2018 11TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2 (2018)

Article Computer Science, Interdisciplinary Applications

A MapReduce-based improvement algorithm for DBSCAN

Xiaojuan Hu et al.

JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY (2018)

Proceedings Paper Computer Science, Information Systems

Semi-supervised Learning on Graphs with Generative Adversarial Nets

Ming Ding et al.

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (2018)

Proceedings Paper Computer Science, Artificial Intelligence

New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine

Bin Gu et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Computer Science, Information Systems

Configuration-Based Fingerprinting of Mobile Device Using Incremental Clustering

Zhijun Ding et al.

IEEE ACCESS (2018)

Article Geochemistry & Geophysics

A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification

Zengmao Wang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2017)

Article Computer Science, Artificial Intelligence

Adaptive Ensembling of Semi-Supervised Clustering Solutions

Zhiwen Yu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)

Article Medicine, General & Internal

How to design efficient cluster randomised trials

K. Hemming et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Article Computer Science, Artificial Intelligence

Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering

Shibing Zhou et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

Cluster Validation Method for Determining the Number of Clusters in Categorical Sequences

Gongde Guo et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Article Medicine, General & Internal

How to design efficient cluster randomised trials

K. Hemming et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Article Computer Science, Information Systems

Initial Shape Pool Construction for Facial Landmark Localization Under Occlusion

Xinrong Wu et al.

IEEE ACCESS (2017)

Article Computer Science, Information Systems

An effective and efficient hierarchical K-means clustering algorithm

Jianpeng Qi et al.

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS (2017)

Proceedings Paper Computer Science, Information Systems

Incremental Clustering for Semi-Supervised Anomaly Detection applied on Log Data

Markus Wurzenberger et al.

PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY (ARES 2017) (2017)

Article Computer Science, Theory & Methods

A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning

Nadia Felix F. Da Silva et al.

ACM COMPUTING SURVEYS (2016)

Article Geochemistry & Geophysics

Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images

Hongyan Zhang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)

Article Computer Science, Information Systems

Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions

Bruce Hajek et al.

IEEE TRANSACTIONS ON INFORMATION THEORY (2016)

Article Computer Science, Artificial Intelligence

Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering

Zhiwen Yu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2016)

Article Multidisciplinary Sciences

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

Yordan P. Raykov et al.

PLOS ONE (2016)

Article Geochemistry & Geophysics

Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery

Ailong Ma et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2015)

Article Computer Science, Artificial Intelligence

Joint Group Sparse PCA for Compressed Hyperspectral Imaging

Zohaib Khan et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)

Article Biochemical Research Methods

Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data

Zhiwen Yu et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2015)

Article Multidisciplinary Sciences

The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials

Stephen A. Lauer et al.

PLOS ONE (2015)

Proceedings Paper Computer Science, Cybernetics

A Multiscale Spectral Method for Learning Number of Clusters

Anna Little et al.

2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (2015)

Proceedings Paper Computer Science, Interdisciplinary Applications

Estimation of Clusters Number and Initial Centers of K-means Algorithm Using Watershed Method

Xiaolong Wang et al.

14TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS, ENGINEERING AND SCIENCE (DCABES 2015) (2015)

Article Biochemical Research Methods

Determining Semantically Related Significant Genes

Kamal Taha

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2014)

Article Computer Science, Artificial Intelligence

The MinMax k-Means clustering algorithm

Grigorios Tzortzis et al.

PATTERN RECOGNITION (2014)

Article Multidisciplinary Sciences

Clustering by fast search and find of density peaks

Alex Rodriguez et al.

SCIENCE (2014)

Article Computer Science, Information Systems

A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

Adil Fahad et al.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING (2014)

Article Biochemical Research Methods

A novel hierarchical clustering algorithm for gene sequences

Dan Wei et al.

BMC BIOINFORMATICS (2012)

Article Computer Science, Information Systems

Scalable K-Means++

Bahman Bahmani et al.

PROCEEDINGS OF THE VLDB ENDOWMENT (2012)

Article Computer Science, Artificial Intelligence

Modified moving k-means clustering algorithm

Mohd Fauzi Alias et al.

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS (2012)

Article Health Care Sciences & Services

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters

Karla Hemming et al.

BMC MEDICAL RESEARCH METHODOLOGY (2011)

Article Computer Science, Artificial Intelligence

SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

Ibai Gurrutxaga et al.

PATTERN RECOGNITION (2010)

Article Automation & Control Systems

Achieving Microaggregation for Secure Statistical Databases Using Fixed-Structure Partitioning-Based Learning Automata

Ebaa Fayyoumi et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2009)

Review Computer Science, Artificial Intelligence

A Survey of Evolutionary Algorithms for Clustering

Eduardo Raul Hruschka et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2009)

Article Computer Science, Artificial Intelligence

Modified global k-means algorithm for minimum sum-of-squares clustering problems

Adil M. Bagirov

PATTERN RECOGNITION (2008)

Article Computer Science, Artificial Intelligence

A method for initialising the K-means clustering algorithm using kd-trees

Stephen J. Redmond et al.

PATTERN RECOGNITION LETTERS (2007)

Review Computer Science, Artificial Intelligence

Survey of clustering algorithms

R Xu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2005)

Article Statistics & Probability

Finding the number of clusters in a dataset: An information-theoretic approach

CA Sugar et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2003)

Article Statistics & Probability

Class prediction by nearest shrunken centroids, with applications to DNA microarrays

R Tibshirani et al.

STATISTICAL SCIENCE (2003)

Article Computer Science, Artificial Intelligence

Stochastic K-means algorithm for vector quantization

B Kövesi et al.

PATTERN RECOGNITION LETTERS (2001)

Article Computer Science, Artificial Intelligence

GA-fuzzy modeling and classification: Complexity and performance

M Setnes et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2000)