Germana Scepi

PARAMETRIC AND NON PARAMETRIC MULTIVARIATE QUALITY CONTROL CHARTS

This contribution aims at analysing the problem of monitoring products/processes of multivariate quality measurements. Classical and parametric approaches are discussed together with new and non parametric ones. We focus particularly on the non parametric control schemes both for simple data as well as for batch and time dependent data. The advantages and drawbacks of the different approaches are compared by means of some examples and applications. Finally, some new developments on the control of complex and structured data, like symbolic data, are introduced.

Geostructure of Coroglio tuff cliff, Naples (Italy) derived from terrestrial laser scanner data

We present a long-range terrestrial laser scanner application for the geostructural mapping of Coroglio cliff, a tuff rock face exposed along the coastal zone of Campi Flegrei, Napoli. The procedure includes several phases (geomorphological analysis, structural field survey, laser scanner data acquisition and data processing, 3-D model development and analysis, geostructural classification of discontinuity orientation data and 2-D vertical cartographic production). Field data were processed with specific software dedicated to geostructural and geometric analysis. Spatial data were managed with a geographical information system and have been used for the construction of 2-D and 3-D geometric models of the rock cliff surface and geostructural interpretation. The main product of this study is a vertical geostructural map of the Coroglio cliff at 1:500 scale that illustrates the spatial distribution and orientation of the major families of structural discontinuities detected along the exposed surface of the cliff. The cartographic product includes base information useful to identify the main rock failure mechanisms along the cliff and represents a first step for the zonation of areas susceptible to block failures and the planning of monitoring activities.

Visualizing and Exploring High Frequency Financial Data: Beanplot Time Series

In this paper we deal with the problem of visualizing and exploring specific time series such as high-frequency financial data. These data present unique features, absent in classical time series, which involve the necessity of searching and analysing an aggregate behaviour. Therefore, we define peculiar aggregated time series called beanplot time series. We show the advantages of using them instead of scalar time series when the data have a complex structure. Furthermore, we underline the interpretative proprieties of beanplot time series by comparing different types of aggregated time series. In particular, with simulated and real examples, we illustrate the different statistical performances of beanplot time series respect to boxplot time series.

Time Series Clustering from High Dimensional Data

Due to technological advances there is the possibility to collect datasets of growing size and dimension. On the other hand, standard techniques do not allow the easy management of large dimensional data and new techniques need to be considered in order to find useful results. Another relevant problem is the information loss due to the aggregation in large data sets. We need to take into account this information richness present in the data which could be hidden in the data visualization process. Our proposal - which contributes to the literature on temporal data mining - is to use some new types of time series defined as the beanplot time series in order to avoid the aggregation and to cluster original high dimensional time series effectively. In particular we consider the case of high dimensional time series and a clustering approach based on the statistical features of the beanplot time series.

An Approach to Forecasting Beanplot Time Series

Visualization and Forecasting of time series data is difficult when the data are very numerous, with complex structures as, for example, in the presence of high volatility and structural changes. This is the case of high frequency data or, in general, of financial data, where we cannot clearly visualize the single data and where the necessity of an aggregation arises. In this paper we deal with the specific problem of forecasting beanplot time series. We propose an approach based firstly on a parameterization of the beanplot time series and successively on the chosen best forecasting method with respect to our data. In particular we experiment with a strategy to use combination forecast methods in order to improve the forecasting performance.

Network Data as Contiguity Constraints in Modeling Preference Data

In the last decades the use of regression-like preference models has found widespread application in marketing research. The Conjoint Analysis models have even more been used to analyze consumer preferences and simulate product positioning. The typical data structure of this kind of models can be enriched by the presence of supplementary information observed on respondents. We suppose that relational data observed on pairs of consumers are available. In such a case, the existence of a consumer network is introduced in the Conjoint model as a set of contiguous constraints among the respondents. The proposed approach will allow to bring together the theoretical framework of Social Network Analysis with the explicative power of Conjoint Analysis models. The combined use of relational and choice data could be usefully exploited in the framework of relational and tribal marketing strategies.

Clustering Algorithms for Large Temporal Data Sets

Temporal Data Mining is a rapidly evolving and new area of research that is at the intersection of several disciplines, including statistics, temporal pattern recognition, optimisation, visualisation, high-performance computing, and parallel computing. This paper is intended to serve a discussion on a specific Temporal Data Mining task: Temporal Cluster Analysis. Most clustering algorithms of the traditional type are severely limited in dealing with large temporal data sets. Therefore we discuss the applicability of clustering algorithms to these data sets. This paper is enriched with an application of a new algorithm on a real sequential database.

Combining different evaluation systems on social media for measuring user satisfaction

Abstract Web 2.0 allows people to express and share their opinions about products and services they buy/use. These opinions can be expressed in various ways: numbers, texts, emoticons, pictures, videos, audios, and so on. There has been great interest in the strategies for extracting, organising and analysing this kind of information. In a social media mining framework, in particular, the use of textual data has been explored in depth and still represents a challenge. On a rating and review website, user satisfaction can be detected both from a rating scale and from the written text. However, in common practice, there is a lack of algorithms able to combine judgments provided with both comments and scores. In this paper we propose a strategy to jointly measure the user evaluations obtained from the two systems. Text polarity is detected with a sentiment-based approach, and then combined with the associated rating score. The new rating scale has a finer granularity. Moreover, also enables the reviews to be ranked. We show the effectiveness of our proposal by analysing a set of reviews about the Uffizi Gallery in Florence (Italy) published on TripAdvisor.

Factor PD-Co-clustering on Textual Data

In this paper we propose to extend factor probabilistic distance (FPD) clustering to FPDco-clustering for frequency data. FPD-clustering transforms the data using a factor decomposition and clusters the transformed data optimizing the same criterion. FPDco-clustering simultaneously finds clusters of rows and column basing on the PD-clustering criterion. The method is useful in case of large data sets. In this paper the new method is applied on large textual data sets with the aim of extracting interesting information.

Visualization and Analysis of Multiple Time Series by Beanplot PCA

Beanplot time series have been introduced by the authors as an aggregated data representation, in terms of peculiar symbolic data, for dealing with large temporal datasets. In the presence of multiple beanplot time series it can be very interesting for interpretative aims to find useful syntheses. Here we propose an extension, based on PCA, of the previous approach to multiple beanplot time series. We show the usefulness of our proposal in the context of the analysis of different financial markets.