4.6 Article

Two-Stage Sparse Representation Clustering for Dynamic Data Streams

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering

Yanni Li et al.

Summary: This paper proposes a fully online data stream clustering algorithm called ESA-Stream, which can dynamically learn parameters in a self-adaptive manner, speed up dimensionality reduction, and effectively and efficiently cluster data streams in an online and dynamic environment. Experimental results on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

Stream-based active learning for sliding windows under the influence of verification latency

Tuan Pham et al.

Summary: Existing stream-based active learning methods may deteriorate or fail under the influence of verification latency, which is the time taken for an oracle to provide a queried label. In order to address this issue, a proposed method called Forgetting and Simulating (FS) aims to forget outdated information and simulate delayed labels to provide more realistic estimates of utility.

MACHINE LEARNING (2022)

Article Automation & Control Systems

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Jinping Sui et al.

Summary: In this study, a novel sparse representation-based data stream clustering algorithm called EDSSC is proposed. By considering the time-varying nature of subspace evolution, it can handle evolving data streams in real-time and improve clustering accuracy. Experimental results demonstrate that EDSSC outperforms other online SC methods.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Review Computer Science, Artificial Intelligence

Data stream clustering: a review

Alaettin Zubaroglu et al.

Summary: The number of connected devices generating data streams is increasing steadily, sparking interest in real-time processing despite challenges. Clustering is a suitable method for real-time data stream processing, requiring less prior information and no labeled instances. Data stream clustering presents unique challenges compared to traditional clustering, including concept drift, data structures, and outlier detection.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

Article Computer Science, Information Systems

An adaptive algorithm for dealing with data stream evolution and singularity

Yong-ming Wu et al.

Summary: This paper proposes an adaptive algorithm GNG-L based on GNG for monitoring and tracking drift and singularity of real-time data streams in non-stationary environments, including weight adaptation, neuron deletion, and generation mechanisms. By analyzing changes in local characteristics and ensuring accurate and fast network topology adjustment during data stream evolution, the algorithm effectively tracks changes in datasets.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Subspace data stream clustering with global and local weighting models

Mohammed Oualid Attaoui et al.

Summary: Subspace clustering is applied to discover clusters embedded in high dimensional data, and the S2G-Stream algorithm proposed in this paper proves effective in detecting relevant features and providing the best data partitioning.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Statistical hierarchical clustering algorithm for outlier detection in evolving data streams

Dalibor Krleza et al.

Summary: Anomaly detection in data streams is typically solved in the online phase, while good macro-clustering is produced in the offline phase, making it challenging for two-phase clustering algorithms to equally excel in both anomaly detection and macro-clustering. The proposed statistical hierarchical clustering algorithm aims to address this issue, by using statistical inference on input data streams and constantly updating statistical distributions to adaptively classify data without the need for prior information on the number of clusters or outliers. Testing against typical clustering algorithms demonstrated the universality and quality of the proposed algorithm.

MACHINE LEARNING (2021)

Article Computer Science, Information Systems

An incremental density-based clustering framework using fuzzy local clustering

Sirisup Laohakiat et al.

Summary: The FIDC is an incremental density-based clustering framework that utilizes a one-pass scheme to effectively process large datasets with reduced computation time and memory usage. By employing fuzzy local clustering and a modified valley seeking algorithm, FIDC improves clustering performance and simplifies parameter selection process.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Generalized Latent Multi-View Subspace Clustering

Changqing Zhang et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

MVStream: Multiview Data Stream Clustering

Ling Huang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Deep Subspace Clustering

Xi Peng et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Automation & Control Systems

Dynamic Cluster Formation Game for Attributed Graph Clustering

Zhan Bu et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Article Automation & Control Systems

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Conor Fahy et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Article Computer Science, Artificial Intelligence

Fuzzy Double C-Means Clustering Based on Sparse Self-Representation

Jing Gu et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2018)

Review Computer Science, Artificial Intelligence

An evaluation of data stream clustering algorithms

Stratos Mansalis et al.

STATISTICAL ANALYSIS AND DATA MINING (2018)

Article Computer Science, Artificial Intelligence

Symmetric low-rank preserving projections for subspace learning

Jie Chen et al.

NEUROCOMPUTING (2018)

Article Computer Science, Information Systems

Fully online clustering of evolving data streams into arbitrarily shaped clusters

Richard Hyde et al.

INFORMATION SCIENCES (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

Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis

Chenglong Bao et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)

Article Computer Science, Hardware & Architecture

MuDi-Stream: A multi density clustering algorithm for evolving data stream

Amineh Amini et al.

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2016)

Article Computer Science, Artificial Intelligence

Symmetric low-rank representation for subspace clustering

Jie Chen et al.

NEUROCOMPUTING (2016)

Review Computer Science, Artificial Intelligence

A survey on data stream clustering and classification

Hai-Long Nguyen et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2015)

Article Computer Science, Artificial Intelligence

Data Stream Clustering with Affinity Propagation

Xiangliang Zhang et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)

Article Computer Science, Theory & Methods

Data Stream Clustering: A Survey

Jonathan A. Silva et al.

ACM COMPUTING SURVEYS (2013)

Article Computer Science, Artificial Intelligence

Robust Recovery of Subspace Structures by Low-Rank Representation

Guangcan Liu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Computer Science, Artificial Intelligence

Sparse Subspace Clustering: Algorithm, Theory, and Applications

Ehsan Elhamifar et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Computer Science, Artificial Intelligence

Graph Regularized Sparse Coding for Image Representation

Miao Zheng et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2011)

Article Computer Science, Artificial Intelligence

The ClusTree: indexing micro-clusters for anytime stream mining

Philipp Kranen et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2011)

Article Computer Science, Artificial Intelligence

Incremental clustering of dynamic data streams using connectivity based representative points

Sebastian Luehr et al.

DATA & KNOWLEDGE ENGINEERING (2009)

Article Computer Science, Information Systems

Stream Data Clustering Based on Grid Density and Attraction

Li Tu et al.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2009)

Article Mathematics, Applied

Iterative Thresholding for Sparse Approximations

Thomas Blumensath et al.

JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS (2008)

Article Computer Science, Theory & Methods

A tutorial on spectral clustering

Ulrike von Luxburg

STATISTICS AND COMPUTING (2007)

Article Mathematics, Applied

For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution

DL Donoho

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS (2006)