Advances in Data Analysis and Classification最新文献_第6页

Addressing class imbalance in functional data clustering 解决功能数据聚类中的类不平衡问题

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-11-14 DOI: 10.1007/s11634-024-00611-8

Catherine Higgins, Michelle Carey

{"title":"Addressing class imbalance in functional data clustering","authors":"Catherine Higgins, Michelle Carey","doi":"10.1007/s11634-024-00611-8","DOIUrl":"10.1007/s11634-024-00611-8","url":null,"abstract":"<div><p>The goal of functional clustering is twofold: first, to categorize curves with similar temporal behaviors into separate clusters, and second, to obtain a representative curve that summarizes the typical temporal behavior within each cluster. An important challenge in current functional clustering techniques is class imbalance, where some clusters contain a significantly greater number of curves than others. While class imbalance is extensively addressed in supervised classification, it remains relatively unexplored in unsupervised contexts. To address this gap, we propose adapting the iterative hierarchical clustering approach, originally designed for multivariate data, to the context of functional data. Thus introducing a novel method called functional iterative hierarchical clustering (funIHC) to effectively handle the clustering of imbalanced functional data. Through comprehensive simulation studies and benchmarking datasets, we demonstrate the effectiveness of the funIHC approach. Utilizing funIHC on gene expression data related to human influenza infection induced by the H3N2 virus, we identify five distinct and biologically meaningful patterns of gene expression. The R and MATLAB code for implementing funIHC is freely accessible at www.fdaatucd.com.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"1023 - 1050"},"PeriodicalIF":1.3,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterisation and calibration of multiversal methods 通用方法的表征和校准

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-11-12 DOI: 10.1007/s11634-024-00610-9

Giulio Giacomo Cantone, Venera Tomaselli

{"title":"Characterisation and calibration of multiversal methods","authors":"Giulio Giacomo Cantone, Venera Tomaselli","doi":"10.1007/s11634-024-00610-9","DOIUrl":"10.1007/s11634-024-00610-9","url":null,"abstract":"<div><p>Multiverse Analysis is a heuristic for robust multiple models estimation where data fit many connected specifications of the same abstract model, instead of a singular or a small selection of specifications. Differently from the canonical application of multimodels, in Multiverse Analysis the probabilities of the specifications to be included in the analysis are never assumed independent of each other. Grounded in this consideration, this study provides a compact statistical characterisation of the process of elicitation of the specifications in Multiverse Analysis and conceptually adjacent methods, connecting previous insights from meta-analytical Statistics, model averaging, Network Theory, Information Theory, and Causal Inference. The calibration of the multiversal estimates is treated with references to the adoption of Bayesian Model Averaging vs. alternatives. In the applications, it is checked the theory that Bayesian Model Averaging reduces both error and uncertainty for well-specified multiversal models but amplifies errors when a collider variable is included in the multiversal model. In well-specified models, alternatives do not perform better than Uniform weighting of the estimates, so the adoption of a gold standard remains ambiguous. Normative implications for misinterpretation of Multiverse Analysis and future directions of research are discussed.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"989 - 1021"},"PeriodicalIF":1.3,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00610-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612702","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

A sparse exponential family latent block model for co-clustering 一种稀疏指数族潜块共聚类模型

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-10-26 DOI: 10.1007/s11634-024-00608-3

Saeid Hoseinipour, Mina Aminghafari, Adel Mohammadpour, Mohamed Nadif

引用次数: 0

Identifying influential observations in concurrent functional regression with weighted bootstrap 用加权自举法识别并发函数回归中有影响的观测值

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-10-17 DOI: 10.1007/s11634-024-00607-4

Ryan D. Pittman, David B. Hitchcock

{"title":"Identifying influential observations in concurrent functional regression with weighted bootstrap","authors":"Ryan D. Pittman, David B. Hitchcock","doi":"10.1007/s11634-024-00607-4","DOIUrl":"10.1007/s11634-024-00607-4","url":null,"abstract":"<div><p>Metrics such as <i>DFBETAS</i>, <i>DFFITS</i>, and Cook’s Distance are used in ordinary linear regression to assess the influence each individual observation has on the fitted model. Here we quantify the influence of functional observations in the concurrent functional linear model where both predictor and response are functional objects, presenting four measures to identify which functional observations are the most influential on the results. We introduce a novel weighted bootstrapping with perturbations method to identify when an observation is significantly influential on the fitted functional regression model. We show this method’s validity using a simulation study and two real data examples. In the first example, our new measures are deployed on paired river heights during ten past flood events. Our method identifies which of the ten past flood observations has the most impact (and whether it is significant) on the functional linear model. These results also show which flood event most alters the relationship between the two river heights. The second example involves concurrently observed air and water temperature functions at 35 locations across the United States coastline. Our method identifies the location(s) with significant influence on the functional linear model.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 :","pages":"197 - 226"},"PeriodicalIF":1.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Bayesian inference for the censored mixture of experts model using heavy-tailed distributions 使用重尾分布的剔除混合专家模型的鲁棒贝叶斯推断

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-10-17 DOI: 10.1007/s11634-024-00609-2

Elham Mirfarah, Mehrdad Naderi, Tsung-I Lin, Wan-Lun Wang

{"title":"Robust Bayesian inference for the censored mixture of experts model using heavy-tailed distributions","authors":"Elham Mirfarah, Mehrdad Naderi, Tsung-I Lin, Wan-Lun Wang","doi":"10.1007/s11634-024-00609-2","DOIUrl":"10.1007/s11634-024-00609-2","url":null,"abstract":"<div><p>Statistical analysis of censored data has received considerable interest across various fields, including biomedical, clinical and econometrical studies. The cause of censored measurement is usually induced by the limitation of measuring instruments and/or experimental design. In practice, regression modeling for censored data might encounter departure from normality of errors due primarily to the latent sources of heterogeneity, and/or the presence of atypical observations and outliers. A Bayesian analysis for the mixture of linear experts model is studied wherein the errors follow the scale mixture of normal distribution, and the responses suffer from either a left or right censoring schemes. We propose a weakly informative prior structure for the parameters and show that the corresponding posterior distributions are proper. Leveraging the Ultimate Pólya-Gamma data-augmentation method, we efficiently sample gating parameters and consequentially allocate cluster memberships. Compared to the traditional maximum likelihood method, our Bayesian approach is shown to mitigate the impact of censoring on deteriorating estimation and classification abilities. The effectiveness of our proposal is illustrated by undertaking some synthetic studies and a real data example. <span>(texttt{R})</span> scripts for the implementation of our Bayesian methods are available at the <span>(texttt{GitHub})</span> repository.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"921 - 949"},"PeriodicalIF":1.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns 加密货币收益分布的非对称拉普拉斯尺度混合

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-10-05 DOI: 10.1007/s11634-024-00606-5

Antonio Punzo, Luca Bagnato

{"title":"Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns","authors":"Antonio Punzo, Luca Bagnato","doi":"10.1007/s11634-024-00606-5","DOIUrl":"10.1007/s11634-024-00606-5","url":null,"abstract":"<div><p>Recent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector <span>(varvec{theta })</span> providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of <span>(varvec{theta })</span>. We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 classification and related methods”","pages":"275 - 322"},"PeriodicalIF":1.3,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00606-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161972","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

Special issue on “New methodologies in clustering and classification for complex and/or big data” 关于 "复杂和/或海量数据的聚类和分类新方法 "的特刊

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-09-04 DOI: 10.1007/s11634-024-00605-6

Paula Brito, Andrea Cerioli, Luis Angel García-Escudero, Gilbert Saporta

引用次数: 0

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks 用于分析纵向双方位网络的具有特定个体效应的边际模型

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-09-03 DOI: 10.1007/s11634-024-00604-7

Francesco Bartolucci, Antonietta Mira, Stefano Peluso

{"title":"Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks","authors":"Francesco Bartolucci, Antonietta Mira, Stefano Peluso","doi":"10.1007/s11634-024-00604-7","DOIUrl":"10.1007/s11634-024-00604-7","url":null,"abstract":"<div><p>A new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of the binary variables indicating if each single actor is involved or not in an event. The adopted parametrization is based on first- and second-order effects, formulated as in marginal models for categorical data and free higher order effects. In particular, second-order effects are log-odds ratios with meaningful interpretation from the social perspective in terms of tendency to cooperate, in contrast to first-order effects interpreted in terms of tendency of each single actor to participate in an event. These effects are parametrized on the basis of the event times, so that suitable latent trajectories of individual behaviors may be represented. Inference is based on a composite likelihood function, maximized by an algorithm with numerical complexity proportional to the square of the number of units in the network. A classification composite likelihood is used to cluster the actors, simplifying the interpretation of the data structure. The proposed approach is illustrated on simulated data and on a dataset of scientific articles published in four top statistical journals from 2003 to 2012.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"895 - 920"},"PeriodicalIF":1.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00604-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209751","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

Using Bagging to improve clustering methods in the context of three-dimensional shapes 在三维图形中使用套袋法改进聚类方法

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-08-21 DOI: 10.1007/s11634-024-00602-9

Inácio Nascimento, Raydonal Ospina, Getúlio Amorim

{"title":"Using Bagging to improve clustering methods in the context of three-dimensional shapes","authors":"Inácio Nascimento, Raydonal Ospina, Getúlio Amorim","doi":"10.1007/s11634-024-00602-9","DOIUrl":"10.1007/s11634-024-00602-9","url":null,"abstract":"<div><p>Cluster Analysis techniques are a common approach to classifying objects within a dataset into distinct clusters. The clustering of geometric shapes of objects holds significant importance in various fields of study. To analyze the geometric shapes of objects, researchers often employ Statistical Shape Analysis methods, which retain crucial information after accounting for scaling, locating, and rotating an object. Consequently, several researchers have focused on adapting clustering algorithms for shape analysis. Recently, three-dimensional (3D) shape clustering has become crucial for analyzing, interpreting, and effectively utilizing 3D data across diverse industries, including medicine, robotics, civil engineering, and paleontology. In this study, we adapt the <i>K-means</i>, <i>CLARANS</i> and <i>Hill Climbing</i> methods using an approach based on the <i>Bagging</i> procedure to achieve enhanced clustering accuracy. We conduct simulation experiments for both isotropy and anisotropy scenarios, considering various dispersion variations. Furthermore, we apply the proposed approach to real datasets from relevant literature. We evaluate the obtained clusters using cluster validation measures, specifically the Rand Index and the Fowlkes-Mallows Index. Our results demonstrate substantial improvements in clustering quality when implementing the <i>Bagging</i> approach in conjunction with the <i>K-means</i>, <i>CLARANS</i> and <i>Hill Climbing</i> methods. The combination of the Bagging method and clustering algorithms provided substantial gains in the quality of the clusters.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"861 - 893"},"PeriodicalIF":1.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The chiPower transformation: a valid alternative to logratio transformations in compositional data analysis chiPower 转换：组合数据分析中对数转换的有效替代方案

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-08-01 DOI: 10.1007/s11634-024-00600-x

Michael Greenacre

{"title":"The chiPower transformation: a valid alternative to logratio transformations in compositional data analysis","authors":"Michael Greenacre","doi":"10.1007/s11634-024-00600-x","DOIUrl":"10.1007/s11634-024-00600-x","url":null,"abstract":"<div><p>The approach to analysing compositional data has been dominated by the use of logratio transformations, to ensure exact subcompositional coherence and, in some situations, exact isometry as well. A problem with this approach is that data zeros, found in most applications, have to be replaced to allow the logarithmic transformation. An alternative new approach, called the ‘chiPower’ transformation, which allows data zeros, is to combine the standardization inherent in the chi-square distance in correspondence analysis, with the essential elements of the Box-Cox power transformation. The chiPower transformation is justified because it defines between-sample distances that tend to logratio distances for strictly positive data as the power parameter tends to zero, and are then equivalent to transforming to logratios. For data with zeros, a value of the power can be identified that brings the chiPower transformation as close as possible to a logratio transformation, without having to substitute the zeros. Especially in the area of high-dimensional data, this alternative approach can present such a high level of coherence and isometry as to be a valid approach to the analysis of compositional data. Furthermore, in a supervised learning context, if the compositional variables serve as predictors of a response in a modelling framework, for example generalized linear models, then the power can be used as a tuning parameter in optimizing the accuracy of prediction through cross-validation. The chiPower-transformed variables have a straightforward interpretation, since they are identified with single compositional parts, not ratios.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 3","pages":"769 - 796"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0