Clustering Algorithm

Clustering algorithms aim to group similar data points together, revealing underlying structure and patterns within datasets. Current research emphasizes improving scalability and efficiency for large datasets, exploring novel approaches like those based on graph neural networks, cellular automata, and dimensionality reduction techniques such as UMAP, often combined with established methods like k-means and DBSCAN. These advancements are crucial for diverse applications, ranging from image analysis and text processing to high-energy physics and urban planning, enabling more effective data exploration and knowledge discovery.

Papers

August 15, 2022

POCS-based Clustering Algorithm
Le-Anh Tran, Henock M. Deberneh, Truong-Dong Do, Thanh-Dat Nguyen, My-Ha Le, Dong-Chul Park
Data Clustering Clustering Algorithm Fuzzy C Mean Conventional Clustering

August 8, 2022

Clustering Optimisation Method for Highly Connected Biological Data
Richard Tjörnhammar
Clustering Algorithm Data Driven Discovery Heterogeneous Clinical Clustering Result

July 14, 2022

Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach
Dimitrios Saligkaras, Vasileios E. Papageorgiou
Raw Data Unsupervised Learning Clustering Method Clustering Algorithm Unsupervised Model Great Truth New Event Cluster

May 31, 2022

Secure Federated Clustering
Songze Li, Sizai Hou, Baturalp Buyukates, Salman Avestimehr
Hierarchical Clustering K$ Mean Clustering Algorithm Private Clustering Centralized Data

April 22, 2022

Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Takato Kinoshita, Naoki Masuyama, Yiping Liu, Yusuke Nojima, Hisao Ishibuchi
Evolutionary Algorithm Clustering Algorithm Many Objective Optimization Adaptive Resonance Decomposition Based Direct Mating

April 18, 2022

A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates
Akifumi Okuno, Kohei Hattori
Relevant Covariates Clustering Algorithm Parameter Uncertainty Uncertainty Set Greedy SLIM Clustering Problem Incremental Clustering

March 23, 2022

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering Algorithm Distance Metric Logical Constraint Constrained Clustering Kernel Learning Constraint Relaxation Pairwise Constraint

March 15, 2022

Natural Hierarchical Cluster Analysis by Nearest Neighbors with Near-Linear Time Complexity
Kaan Gokcesu, Hakan Gokcesu
Data Clustering Nearest Neighbor Hierarchical Clustering Clustering Algorithm Linear Complexity Divisive Hierarchical Clustering Algorithm

February 16, 2022

Using the left Gram matrix to cluster high dimensional data
Shahina Rahman, Valen E. Johnson, Suhasini Subba Rao
High Dimensional Data Clustering Algorithm Gram Matrix Microarray Data

February 15, 2022

K-Means for Noise-Insensitive Multi-Dimensional Feature Learning
Nicholas Pellegrino, Paul Fieguth, Parsin Haji Reza
K Mean Clustering Algorithm Noise Robust Learning Synthetic Signal Time Domain Measurement Modality Photoacoustic Microscopy

January 7, 2022

Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning
Zecang Gu, Xiaoqi Sun, Yuan Sun, Fuquan Zhang
Full Model Clustering Algorithm Unsupervised Clustering Independent Learning Self Stimulatory Spatial Clustering Probability Space

December 16, 2021

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations
Szymon Bobek, Michał Kuk, Jakub Brzegowski, Edyta Brzychczy, Grzegorz J. Nalepa
Constructive Approach Line by Line Explanation Data Clustering Hierarchical Clustering Background Knowledge Clustering Algorithm Cluster Analysis Knowledge Augmented

November 20, 2021

Feature selection or extraction decision process for clustering using PCA and FRSD
Jean-Sebastien Dessureault, Daniel Massicotte
Feature Selection Dimensionality Reduction Principal Component Analysis K Mean Clustering Algorithm Extraction Method

December 31, 2018

A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Diego Ulisse Pizzagalli, Santiago Fernandez Gonzalez, Rolf Krause
Microscopy Image Human Machine Shortest Path Clustering Algorithm Complex Datasets

Clustering Algorithm

Papers

POCS-based Clustering Algorithm

Clustering Optimisation Method for Highly Connected Biological Data

Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach

Secure Federated Clustering

Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization

A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation

Natural Hierarchical Cluster Analysis by Nearest Neighbors with Near-Linear Time Complexity

Using the left Gram matrix to cluster high dimensional data

K-Means for Noise-Insensitive Multi-Dimensional Feature Learning

Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

Feature selection or extraction decision process for clustering using PCA and FRSD

A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets