Catálogo de publicaciones - libros

Mining association rules is well established in quantitative business research literature and makes up an up-and-coming topic in marketing practice. However, reducing the analysis to the assessment and interpretation of a few selected rules does not provide a complete picture of the data structure revealed by the rules.

This paper introduces a new approach of visualizing relations between items by assigning them to a rectangular grid with respect to their mutual association. The visualization task leads to a quadratic assignment problem and is tackled by means of a genetic algorithm. The methodology is demonstrated by evaluating a set of rules describing marketing practices in Russia.

In this paper we propose three generalizations of well-known N-gram approaches for measuring similarity of single-line melodies. In a former paper we compared around 50 similarity measures for melodies with empirical data from music psychological experiments. Similarity measures based on edit distances and N-grams always showed the best results for different contexts. This paper aims at a generalization of N-gram measures that can combine N-gram and other similarity measures in a fairly general way.

This paper describes an empirical approach to evaluating similarity measures for the comparision of two note sequences or melodies. In the first sections the experimental approach and the empirical results of previous studies on melodic similarity are reported. In the discussion section several questions are raised that concern the nature of similarity or distance measures for melodies and musical material in general. The approach taken here is based on an empirical comparision of a variety of similarity measures with experimentally gathered rating data from human music experts. An optimal measure is constructed on the basis of a linear model.

Based on a model of Davy and Godsill (2002) we describe a general model for time series from monophonic musical sound to estimate the pitch. The model is a hierarchical Bayes Model which will be estimated with MCMC methods. All the parameters and their prior distributions are motivated individually. For parameter estimation an MCMC based stochastic optimization is introduced. In a simulation study it will be looked for the best implementation of the optimization procedure.

Investigating a data set containing different sounds of several instruments suggests that local modelling may be a promising approach to take into account different timbre characteristics of different instruments. For this reason, some basic ideas towards such a local modelling are realized in this paper yielding a framework for further studies.

The gene expression data is characterized by its considerably great amount of features in comparison to the number of observations. The direct use of traditional statistics techniques of supervised classification can give poor results in gene expression data. Therefore, dimension reduction is recommendable prior to the application of a classifier. In this work, we propose a method that combines two types of dimension reduction techniques: feature selection and feature extraction. First, one of the following feature selection procedures: a univariate ranking based on the Kruskal-Wallis statistic test, the Relief, and recursive feature elimination (RFE) is applied on the dataset. After that, principal components are formed with the selected features. Experiments carried out on eight gene expression datasets using three classifiers: logistic regression, k-nn and rpart, gave good results for the proposed method.

The problem of phylogenetic inference from datasets including incomplete characters is among the most relevant issues in systematic biology. In this paper, we propose a new probabilistic method for estimating unknown nucleotides before computing evolutionary distances. It is developed in the framework of the Tamura-Nei evolutionary model (Tamura and Nei (1993)). The proposed strategy is compared, through simulations, to existing methods “Ignoring Missing Sites” (IMS) and “Proportional Distribution of Missing and Ambiguous Bases” (PDMAB) included in the PAUP package (Swofford (2001)).

In this article we describe a new method allowing one to predict and visualize possible horizontal gene transfer events. It relies either on a metric or topological optimization to estimate the probability of a horizontal gene transfer between any pair of edges in a species phylogeny. Species classification will be examined in the framework of the complete and partial gene transfer models.