Multiway clustering with time-varying parameters.

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data. [Abstract copyright: © The Author(s) 2022.

A New Technique for Dealing with Complex Stimuli in Conjoint Analysis

This paper deals with the problem of a large number of multi-attributes stimuli in Conjoint Analysis. The aim of this paper is to critically discuss some specific aspects of the bridging technique originally proposed by Bretton and Clark and to propose an innovative approach based on the same philosophy but on a different estimation method. The new technique is based on several estimation steps. It is able to make the most of the orthogonality properties related to the experimental designs. Furthermore, a validation procedure for the bridging results has been proposed. This procedure allows answering to the general question on the reliability of performing a bridging technique

An integrated approach for rock slope failure monitoring: The case study of Coroglio tuff cliff (Naples, Italy) - Preliminary results

The paper re ports the i mple mentation of an integrate d syste m ai me d at the real-ti me monitoring of a series of physical parame ters controlling the r ock slope stability. The system has bee n installe d on the Cor oglio tuff cliff, loc ate d in the highly ur banize d coastal area of Naples (Italy) at the bor der of the acti ve volcanic cal der a of Campi Flegrei. Preliminar y results obtai ne d during the first ye ar of data ac quisition and monitoring acti vi ty (Dece mber 2014 – January 2016) are also discussed on the basis of statistical models. (3) (PDF) An integrated approach for rock slope failure monitoring: the case study of Coroglio tuff cliff (Naples, Italy) – preliminary results. Available from: https://www.researchgate.net/publication/299340773_An_integrated_approach_for_rock_slope_failure_monitoring_the_case_study_of_Coroglio_tuff_cliff_Naples_Italy_-_preliminary_results [accessed Feb 27 2020].Published242-2471IT. Reti di monitoraggio e sorveglianzaN/A or not JC

A weighted k-medoids algorithm for clustering time series' projections

Time-series clustering is one of the most common techniques used to discover similar structures in a dataset with dynamic objects. The main issue in time series clustering lies in the computation of a proper distance. A lot of approaches, based on statistical model parameters or on time series features, have been proposed in the literature. Some clustering approaches do not consider as units the single time series but their projections. In this case, it is very important to define a peculiar distance, taking into account the characteristics of the observations. In this framework, we propose a kMEDOID-type algorithm based on an optimal weighting scheme for multiple distances. The weights, obtained by minimizing the weighted squared distance between each i-th object from its k-th medoid, reflect the importance of the information contained in each distance. The performance of the proposed fast algorithm will be evaluated by comparing the results with those obtained using well-known clustering approaches. Furthermore, an application to real datasets is provided