Model Based Clustering

Model-based clustering uses probabilistic models, often Gaussian Mixture Models (GMMs) or their variations (e.g., incorporating skewed distributions or handling time series data), to group data points based on their underlying probability distributions. Current research emphasizes improving the efficiency and stability of algorithms like Expectation-Maximization (EM), developing methods to handle constraints (e.g., metric constraints or missing data), and addressing challenges like determining the optimal number of clusters and handling outliers. These advancements enhance the accuracy and applicability of model-based clustering across diverse fields, including genomics, trajectory analysis, and the analysis of complex datasets with heterogeneous structures.

Papers

July 27, 2024

A simulation study of cluster search algorithms in data set generated by Gaussian mixture models
Ryosuke Motegi, Yoichi Seki
Raw Data Simulation Study Gaussian Mixture Model Model Based Clustering Centroid Detection Case Study

May 28, 2024

MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz, Ni Lao
Model Based Clustering Goodness of Fit

March 18, 2024

A Clustering Method with Graph Maximum Decoding Information
Xinrun Xu, Manying Lv, Zhanbiao Lian, Yurong Wu, Jin Yan, Shan Jiang, Zhiming Ding
Graph Based Clustering Method Graph Partitioning Model Based Clustering Graph Based Clustering Graph Decoder

February 10, 2024

Modeling and predicting students' engagement behaviors using mixture Markov models
R. Maqsood, P. Ceravolo, C. Romero, S. Ventura
Expectation Maximization Student Friendly Knowledge Student Engagement Model Based Clustering Engagement Behavior E Learning Mixture Markov Model

February 8, 2024

Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan
Gaussian Mixture Model Mixture Model Python Library Parsimonious Machine Learning Model Based Clustering

October 11, 2023

Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance
Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp, Anne Roefs
Forecasting Performance Individual Perspective Model Based Clustering Clustering Based Approach Ecological Momentary Assessment Group Wise Prediction Model

October 8, 2023

Clustering Three-Way Data with Outliers
Katharine M. Clark, Paul D. McNicholas
Model Based Clustering Lloyd Algorithm Three Way Matrix Variate

April 30, 2023

Time series clustering based on prediction accuracy of global forecasting models
Ángel López Oriona, Pablo Montero Manso, José Antonio Vilar Fernández
Prediction Accuracy Time Series Clustering Global Forecasting Clustering Performance Model Based Clustering Divisive Hierarchical Clustering Algorithm

April 14, 2023

The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
Rémi Boutin, Pierre Latouche, Charles Bouveyron
Variational Inference Network Programming Individual Representation Graph Structure Variational Graph Model Based Clustering

February 5, 2023

Regularization and Optimization in Model-Based Clustering
Raphael Araujo Sampaio, Joaquim Dias Garcia, Marcus Poggi, Thibaut Vidal
Optimization Purpose K Mean Gaussian Mixture Unsupervised Clustering Model Based Clustering

December 7, 2022

A parallelizable model-based approach for marginal and multivariate clustering
Miguel de Carvalho, Gabriel Martos Venturini, Andrej Svetlošák
Clustering Method Clustering Algorithm Model Parallelism Model Based Clustering

September 29, 2022

Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Yubo Zhuang, Xiaohui Chen, Yun Yang
Gaussian Mixture Model Heterogeneous Data Expectation Maximization Semidefinite Programming Likelihood Based Model Based Clustering

May 27, 2022

Finite mixture of skewed sub-Gaussian stable distributions
Mahdi Teimouri
Model Based Clustering Finite Mixture Skewed Non IID

February 1, 2022

Personalized Federated Learning via Convex Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar
Personalized Federated Learning Model Based Clustering Convex Cost

December 20, 2021

Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki
Mixture Model Model Based Clustering Random Data Missing Not at Random

November 15, 2021

Machine Learning for Genomic Data
Akankshita Dash
Machine Learning Gaussian Mixture Model Hidden Markov Model Gene Expression Data Model Based Clustering

November 3, 2021

Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score
Luca Coraggio, Pietro Coretto
Practical Algorithm Different Cluster Clustering Method Unifying Theory Cluster Analysis Model Based Clustering Quadratic Discriminant Analysis Clustering Quality