Abstract
Modelling extreme data is very important in several application domains, like for instance finance, meteorology, ecology, etc.. This paper addresses the problem of predicting extreme values of a continuous variable. The main distinguishing feature of our target applications resides on the fact that these values are rare. Any prediction model is obtained by some sort of search process guided by a pre-specified evaluation criterion. In this work we argue against the use of standard criteria for evaluating regression models in the context of our target applications. We propose. a new predictive performance metric for this class of problems that our experiments show to perform better in distinguishing models that are more accurate at rare extreme values. This new evaluation metric could be used as the basis for developing better models in terms of rare extreme values prediction.

Abstract
We propose a Multi-Agent framework to analyze the dynamics of organizational survival in cooperation networks. Firms can decide to cooperate horizontally (in the same market) or vertically with other firms that belong to the supply chain. Cooperation decisions are based on economic variables. We have defined a variant of the density dependence model to set up the dynamics of the survival in the simulation. To validate our model, we have used empirical outputs obtained in previous studies from the automobile manufacturing sector. We have observed that firms and networks proliferate in the regions with lower marginal costs, but new networks keep appearing and disappearing in regions with higher marginal costs.

Abstract
This paper introduces a partitioning clustering method for objects described by interval data. It follows the dynamic clustering approach and uses an L-2 distance. Particular emphasis is put on the standardization problem where we propose and investigate three standardization techniques for interval-type variables. Moreover, various tools for cluster interpretation are presented and illustrated by simulated and real-case data.

Abstract
This paper compares different approaches to the multivariate analysis of interval data, focusing on discriminant analysis. Three fundamental approaches are considered. The first approach assumes an uniform distribution in each observed interval, derives the corresponding measures of dispersion and association, and appropriately defines linear combinations of interval variables that maximize the usual discriminant criterion. The second approach expands the original data set into the set of all interval description vertices, and proceeds with a classical analysis of the expanded set. Finally, a third approach replaces each interval by a midpoint and range representation. Resulting representations, using intervals or single points, are discussed and distance based allocation rules are proposed. The three approaches are illustrated on a real data set.

Abstract

Abstract
Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clustering tendency index value.