Catálogo de publicaciones - libros

Categorization with a very high missing data rate is seldom studied, especially from a non-probabilistic point of view. This paper proposes a new algorithm called that relies on default reasoning and uses the local search paradigm. Two kinds of experiments are considered: the first one presents the results obtained on artificial data sets, the second uses an original and real case where political stereotypes are extracted from newspaper articles at the end of the 19th century.

Several methodologies for function approximation using TSK systems make use of clustering techniques to place the rules in the input space. Nevertheless classical clustering algorithms are more related to unsupervised learning and thus the output of the training data is not taken into account or, simply the characteristics of the function approximation problem are not considered. In this paper we propose a new approach for the initialization of centres in clustering-based TSK systems for function approximation that takes into account the expected output error distribution in the input space to place the fuzzy system rule centres. The convenience of proposed the algorithm comparing to other input clustering and input/output clustering techniques is shown through a significant example.

Weather is an important source of delay for aircraft. Recent studies have shown that certain weather factors have significant influence on air traffic. More than 50% of all delay accounts to weather and causes among others high costs to airlines and passengers. In this work we will show to what extent weather factors in the closer region of Frankfurt Airport have an impact on the delay of flights. Besides the results of a linear regression model we will also present the results of some modern data mining approaches, such as regression trees and fuzzy clustering techniques. With the clustering approach we will show that several weather conditions have a similar influence on the delay of flights. Our analyses focus on the delay that will be explicitly caused by weather factors in the vicinity of the airport, the so-called terminal management area (TMA). Thus, delay caused by weather at the departure airport or by other circumstances during the flight will not bias our results. With our methods it becomes possible to predict the delay of flights if certain weather factors are known. We will specify these factors and quantify their effects on delay.

The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules in order to find structural properties of molecules that determine whether a molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. A promising approach to this task was presented in [2]: an algorithm for finding molecular fragments that discriminate between active and inactive molecules. In this paper we review this approach as well as two extensions: a special treatment of rings and a method to find fragments with wildcards based on chemical expert knowledge.

Modern business face the challenge of to make use of one of there most valuable assets – data about their customers and processes – in real time in order to stay ahead of global competition. In order to achieve real time business intelligence it is necessary to automate data analysis to address the lack of available experts, empower business users and produce analysis results where and when they are required. In this paper we describe how our intelligent data analysis platform SPIDA automatically selects suitable data analysis methods for application to data analysis problems.