Statistical Analysis and Data Mining Special Issue – CLADAG 2019 Special Issue: Selected Papers on Classification and Data Analysis

The journal Statistical Analysis and Data Mining recently published a special issue collecting papers that were originally presented at the 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Cassino, Italy, 11-13 September 2019. The issue, entitled CLADAG 2019 Special Issue: Selected Papers on Classification and Data Analysis, is now available to read here

Portions of the introduction by guest editors Francesca GreselinThomas Brendan MurphyGiovanni C. PorzioDomenico Vistocco are included below: 

The CLADAG group, founded in 1997, promotes advanced methodological research in multivariate statistics with a special vocation in Data Analysis and Classification. CLADAG is a member of the International Federation of Classification Societies (IFCS). It organizes a biennial international scientific meeting, schools related to classification and data analysis, publishes a newsletter, and cooperates with other member societies of the IFCS to the organization of their conferences. Founded in 1985, the IFCS is a federation of national, regional, and linguistically-based classification societies aimed at promoting classification research.

Previous CLADAG meetings were held in Pescara (1997), Roma (1999), Palermo (2001), Bologna (2003), Parma (2005), Macerata (2007), Catania (2009), Pavia (2011), Modena and Reggio Emilia (2013), Cagliari (2015), and Milano (2017).

Best papers from the conference have been submitted to this special issue, and six of them have been selected for publication, following a blind peer-review process. The manuscripts deal with different data analysis issues: mixture of distributions, compositional data analysis, Markov chain for web usability, survival analysis, and applications to high-throughput, eye-tracking, and insurance transaction data.

The paper by Dvorák et al. introduces the Clover plot, an easy-to-understand graphical tool that facilitates the appropriate choice of a classifier, to be employed in supervised classification. 

The paper by Lee et al. proposes a parallelization strategy of the Expectation–Maximization (EM) algorithm, with a special focus on the estimation of finite mixtures of flexible distribution such as the canonical fundamental skew t distribution (CFUST).

The EM algorithm is also discussed in the paper by Scrucca. Here, a fast and efficient Modal EM algorithm for identifying the modes of a density estimated through a finite mixture of Gaussian distributions with parsimonious component covariance structures is provided. (Published Open Access)

Motivated by applications in high-throughput compositional data analysis, the paper by Štefelová et al. proposes a data-driven weighting strategy to enhance marker identification through PLS regression with compositional predictors.

The paper by Zammarchi et al. exploits Markov chain to analyse web usability of a University website using eye tracking methodology. (Published Open Access)

Data from a commercial insurance company in the Czech Republic are instead exploited by D. Zapletal to compare the efficacy of some survival analysis models within an insurance transaction framework. 

In brief, this special issue is in line with the CLADAG goal of supporting the interchange of ideas in Classification and Data Analysis. We strongly believe it well represents the scientific characteristics of the CLADAG community, and we invite all readers to join the next CLADAG conference, which will be in Florence, 11–13 September 2021.