Advances in Data Analysis and Classification最新文献_第3页

Calibrated kNN classification via second-layer neighborhood analysis 通过第二层邻域分析校准kNN分类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-09-26 DOI: 10.1007/s11634-025-00654-5

Bastian Pfeifer, Markus Kreuzthaler

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引用次数: 0

Structural equation modeling with factors and composites within the framework of the basic design 结构方程建模用因子和复合材料框架内的基本设计

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-09-08 DOI: 10.1007/s11634-025-00647-4

Arthur Tenenhaus, Michel Tenenhaus, Theo K. Dijkstra

引用次数: 0

Editorial for ADAC issue 3 of volume 19 (2025) ADAC第19卷第3期（2025）社论

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-09-05 DOI: 10.1007/s11634-025-00652-7

Maurizio Vichi, Andrea Cerioli, Hans A. Kestler

引用次数: 0

Increasing biases can be more efficient than increasing weights 增加偏差可能比增加权重更有效

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-06-16 DOI: 10.1007/s11634-025-00649-2

Carlo Metta, Marco Fantozzi, Andrea Papini, Gianluca Amato, Matteo Bergamaschi, Andrea Fois, Silvia Giulia Galfrè, Alessandro Marchetti, Michelangelo Vegliò, Maurizio Parton, Francesco Morandin

引用次数: 0

Special issue on “Advances in clustering, classification and related methods” 专题“聚类、分类及相关方法的进展”

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-29 DOI: 10.1007/s11634-025-00645-6

Paolo Giordani, Christian Hennig, Julien Jacques, Carla Rampichini

引用次数: 0

Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm 用进化算法估计动态随机块模型的变分推理

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-23 DOI: 10.1007/s11634-025-00634-9

Luca Brusa, Fulvia Pennoni

引用次数: 0

Comparing flexible modelling approaches: the varying-thresholds model versus quantile regression 比较灵活的建模方法：变阈值模型与分位数回归

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-14 DOI: 10.1007/s11634-025-00635-8

Niccolò Ducci, Leonardo Grilli, Marta Pittavino

{"title":"Comparing flexible modelling approaches: the varying-thresholds model versus quantile regression","authors":"Niccolò Ducci, Leonardo Grilli, Marta Pittavino","doi":"10.1007/s11634-025-00635-8","DOIUrl":"10.1007/s11634-025-00635-8","url":null,"abstract":"<div><p>The varying-thresholds model (VTM) is a novel methodology proposed by Tutz ( Flexible predictive distributions from varying-thresholds modelling. https://doi.org/10.48550/arXiv.2103.13324, arXiv:2103.13324 2021) capable of estimating the whole conditional distribution of a response variable in a regression setting. It can be used for continuous, ordinal and count responses. In this study, conditional quantiles and prediction intervals estimated through VTM are compared with those of quantile regression. The comparison is based on a set of data-generating models to assess the performance of the two methodologies regarding the coverage and width of prediction intervals. The simulation study encompasses settings with several functional forms and types of errors. In addition, a discrete version of the continuous ranked probability score is proposed as a tool to choose the best link function for the binary models used in the fitting of VTM. In summary, the varying-thresholds model is a flexible methodology that can be broadly applied with light assumptions; it is advantageous over quantile regression when the conditional quantile function is misspecified.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 classification and related methods”","pages":"493 - 514"},"PeriodicalIF":1.3,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00635-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised learning from attributed networks 来自属性网络的无监督学习

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-14 DOI: 10.1007/s11634-025-00641-w

Lazhar Labiod, Mohamed Nadif

引用次数: 0

Censored trajectory data clustering using mixture of semi-Markov models with application to targeted marketing 混合半马尔可夫模型的截尾轨迹数据聚类及其在目标营销中的应用

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-05 DOI: 10.1007/s11634-025-00633-w

Geet Lahoti, Chitta Ranjan, Jialei Chen, Samaneh Ebrahimi, Chuck Zhang

{"title":"Censored trajectory data clustering using mixture of semi-Markov models with application to targeted marketing","authors":"Geet Lahoti, Chitta Ranjan, Jialei Chen, Samaneh Ebrahimi, Chuck Zhang","doi":"10.1007/s11634-025-00633-w","DOIUrl":"10.1007/s11634-025-00633-w","url":null,"abstract":"<div><p>A trajectory is a spatio-temporal data instance in which a customer or user moves between a set of discrete states while spending a certain amount of time in each state. Using trajectory data as a proxy for customers’ behavior and performing clustering can help to devise targeted marketing strategies. However, the censoring is often encountered due to the inability to observe the complete trajectories. In addition, the exact number of clusters is often unknown. In this work, we propose a novel mixture model-based clustering methodology to analyze the trajectory data and decipher different user segments based on their behavior. Each cluster is profiled using a semi-Markov model while considering the effect of censoring. Each entity is assigned to a cluster based on its similarity to the cluster’s profile. Entity assignments and cluster profiles are simultaneously inferred using a robust expectation maximization algorithm. In the simulation study, our methodology demonstrates better performance than existing methods. The effectiveness of our methodology is further corroborated using a real data set obtained from an internet music provider, where the obtained clustering results are found to be helpful in devising better marketing strategies to target users in different segments.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 2","pages":"693 - 719"},"PeriodicalIF":2.1,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-025-00633-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: Statistical jump model for mixed-type data with missing data imputation 修正：缺失数据输入的混合型数据的统计跳跃模型

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-05-02 DOI: 10.1007/s11634-025-00638-5

Federico P. Cortese, Antonio Pievatolo

引用次数: 0