4.7 Article

VDPC: Variational density peak clustering algorithm

Related references

Note: Only part of the references are listed.
Article Engineering, Chemical

Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder

Huihui Gao et al.

Summary: This paper proposes a novel monitoring scheme based on hierarchical mode identification strategy and stacked denoising autoencoder (HMI-SDAE) for multimode processes. The scheme achieves automatic and accurate division of multiple steady modes and transition modes through mode division and mode identification, and detects faults using deep nonlinear features and monitoring statistics.

CHEMICAL ENGINEERING SCIENCE (2022)

Article Mathematics

Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder

Feng Yu et al.

Summary: This paper proposes a comprehensive monitoring method based on modified density peak clustering and parallel variational autoencoder (MDPC-PVAE) for multimode processes. The MDPC clustering algorithm can automatically identify the number of modes and divide the process data into multiple modes. The features generated by PVAE follow the Gaussian distribution and the control limits are determined using kernel density estimation (KDE).

MATHEMATICS (2022)

Article Computer Science, Artificial Intelligence

An improved density peak clustering algorithm guided by pseudo labels

Yizhang Wang et al.

Summary: This paper proposes a density peak clustering algorithm guided by pseudo labels to avoid manually specifying parameters. Experimental results show that the algorithm outperforms classical and state-of-the-art clustering algorithms in most cases.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

DenMune: Density peak based clustering using mutual nearest neighbors

Mohamed Abbas et al.

Summary: The novel clustering algorithm DenMune is able to handle clusters of arbitrary shapes, varying densities, and unbalanced data classes effectively. It is based on the mutual nearest neighbor principle and can stably detect and remove noise while detecting target clusters.

PATTERN RECOGNITION (2021)

Article Computer Science, Information Systems

Automatic topography of high-dimensional data sets by non-parametric density peak clustering

Maria D'Errico et al.

Summary: As the capability to generate data increases rapidly, the challenge lies in extracting human-readable and useful information from data sets with high dimensions. Mapping data onto a two or three-dimensional surface is a possible approach to achieve this goal.

INFORMATION SCIENCES (2021)

Article Computer Science, Information Systems

Extreme clustering - A clustering method via density extreme points

Shuliang Wang et al.

Summary: Peak clustering, a density based clustering method, has shortcomings in finding cluster centers in low density clusters and detecting normal points as noises. Extreme clustering, which identifies density extreme points and introduces a noise detection module, overcomes these drawbacks and improves performance significantly.

INFORMATION SCIENCES (2021)

Article Computer Science, Information Systems

Efficient implementation and parallelization of fuzzy density based clustering

Can Atilgan et al.

Summary: This study discusses the efficient implementation of a fuzzy density-based clustering algorithm, introduces a specific algorithm and its parallel version, and conducts experimental tests, showing a wide variety of differences in relative speed-ups.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Relative density-based clustering algorithm for identifying diverse density clusters effectively

Yuying Wang et al.

Summary: IDDC is a novel clustering algorithm based on relative density, which can effectively identify clusters with different densities and performs better in dealing with data sets with uneven density distribution compared to existing algorithms.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

A Domain Adaptive Density Clustering Algorithm for Data With Varying Density Distribution

Jianguo Chen et al.

Summary: The DADC algorithm addresses the limited clustering effect of density peak-based clustering algorithms on data with VDD, ED, and MDDM features, by using domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble. This approach improves clustering results and overcomes the issues of sparse cluster loss and cluster fragmentation in such data.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

McDPC: multi-center density peak clustering

Yizhang Wang et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Automation & Control Systems

Enhancing Density Peak Clustering via Density Normalization

Jian Hou et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (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 Computer Science, Artificial Intelligence

A robust density peaks clustering algorithm with density-sensitive similarity

Xiao Xu et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

A systematic density-based clustering method using anchor points

Yizhang Wang et al.

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

Density peak clustering based on relative density relationship

Jian Hou et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities

Fang Fang et al.

PATTERN RECOGNITION (2020)

Article Geochemistry & Geophysics

Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification

Peicheng Zhou et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Computer Science, Information Systems

Munec: a mutual neighbor-based clustering algorithm

Frederic Ros et al.

INFORMATION SCIENCES (2019)

Article Biochemical Research Methods

Clusterdv: a simple density-based clustering method that is robust, general and automatic

Joao C. Marques et al.

BIOINFORMATICS (2019)

Article Computer Science, Artificial Intelligence

Comparative density peaks clustering

Zejian Li et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Computer Science, Information Systems

Shared-nearest-neighbor-based clustering by fast search and find of density peaks

Rui Liu et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Information Systems

Decentralized Clustering by Finding Loose and Distributed Density Cores

Yewang Chen et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Artificial Intelligence

Density core-based clustering algorithm with dynamic scanning radius

Jiang Xie et al.

KNOWLEDGE-BASED SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Representative points clustering algorithm based on density factor and relevant degree

Di Wu et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2017)

Article Engineering, Electrical & Electronic

Segmentation-Based Projected Clustering of Hyperspectral Images Using Mutual Nearest Neighbour

Anand Mehta et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)

Article Computer Science, Artificial Intelligence

Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy

Liu Yaohui et al.

KNOWLEDGE-BASED SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

Fast density clustering strategies based on the k-means algorithm

Liang Bai et al.

PATTERN RECOGNITION (2017)

Article Computer Science, Information Systems

Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors

Juanying Xie et al.

INFORMATION SCIENCES (2016)

Article Computer Science, Artificial Intelligence

Study on density peaks clustering based on k-nearest neighbors and principal component analysis

Mingjing Du et al.

KNOWLEDGE-BASED SYSTEMS (2016)

Article Computer Science, Artificial Intelligence

Effectively clustering by finding density backbone based-on kNN

Mei Chen et al.

PATTERN RECOGNITION (2016)

Article Multidisciplinary Sciences

Clustering by fast search and find of density peaks

Alex Rodriguez et al.

SCIENCE (2014)

Article Computer Science, Artificial Intelligence

Information-theoretic clustering: A representative and evolutionary approach

Daniel Araujo et al.

EXPERT SYSTEMS WITH APPLICATIONS (2013)

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, Artificial Intelligence

Complex Wavelet Structural Similarity: A New Image Similarity Index

Mehul P. Sampat et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2009)

Article Computer Science, Artificial Intelligence

Normalized Mutual Information Feature Selection

Pablo. A. Estevez et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2009)

Article Computer Science, Artificial Intelligence

A Binary Variable Model for Affinity Propagation

Inmar E. Givoni et al.

NEURAL COMPUTATION (2009)

Article Computer Science, Artificial Intelligence

Robust path-based spectral clustering

Hong Chang et al.

PATTERN RECOGNITION (2008)

Article Biochemical Research Methods

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

Limin Fu et al.

BMC BIOINFORMATICS (2007)

Article Computer Science, Artificial Intelligence

A maximum variance cluster algorithm

CJ Veenman et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2002)