Boolean factors as a means of clustering of interestingness measures of association rules

Measures of interestingness play a crucial role in association rule mining. An important methodological problem, on which several papers appeared in the literature, is to provide a reasonable classification of the measures. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of clustering of the measures. Unlike the existing studies, our method reveals overlapping clusters of interestingness measures. We argue that the overlap between clusters is a desired feature of natural groupings of measures and that because formal concepts are used as factors in Boolean factor analysis, the resulting clusters have a clear meaning and are easy to interpret. We conduct three case studies on clustering of measures, provide interpretations of the resulting clusters and compare the results to those of the previous approaches reported in the literature.     

Improving the Behavior of Intelligent Tutoring Agents with Data Mining

This article presents a novel framework for adapting the behavior of intelligent agents. The framework consists of an extended sequential pattern mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human agents executing a procedural task. The proposed framework has been integrated within the CanadarmTutor, an intelligent tutoring agent aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. We present the results of an evaluation that demonstrates the benefits of this integration to agents acting in ill-defined domains.     

Method for analysis and dynamism of factory structure in automotive manufacturing

A turbulent environment characterized by unsteady economic cycles, customized products, a growing bandwidth of products, an exploding number of variants and shorter product life cycles force manufacturers to permanent adaptation of their factories. Flexible and changeable structures will be required to enable factories dealing with the technological challenges and economic pressure of the future competitively. In order to achieve changeability objectives in manufacturing, a detailed analysis of existing structures and its representative attributes is essential. It is the basis for systematic structure planning of factories. In this paper a method for analyzing the capacitive and technological structure of a factory embedded in a network of manufacturing and its network of suppliers is presented. The synchronization of product and production development under the influence of change is intended. Therefore, the structural views of product and production are specifically in focus of the method. Based on the results of the analysis models an approach of a tool for giving product and production structure dynamism is suggested to investigate the effects and dependencies of change drivers in manufacturing.     

Using Boolean factors for the construction of an artificial neural networks

ABSTRACT

We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.     

Beiträge zur Kenntnis der Bäckereihefe

European Food Research and Technology -     

Examining the interaction of marketing and financing decisions in a dynamic environment Die Analyse der Beziehungen von Marketing- und Finanzierungsentscheidungen in einem dynamischen Umfeld

The market success of a new product critically depends on the marketing strategy that is adopted during the introductory phase of its life cycle. The decision theoretic marketing literature provides useful insights to this problem through the application of new product diffusion models. While most of the diffusion models incorporate only marketing variables such as price or advertising into the adoption rates of the new product, we introduce the issue of financial decision making and argue that the success of a new product not only depends on an optimal marketing mix strategy but also on the financial decisions of a firm. We adopt a simple diffusion model and show that in case with demand uncertainty and limited liability more leverage (a higher debt equity ratio) causes the firm to be more aggressive in the product market, i.e., to reduce the price of the product. Our findings suggest that marketing decisions should not be taken in isolation but should be coordinated with financial variables.