Titlebook: Advances in Data Mining: Applications and Theoretical Aspects; 10th Industrial Conf Petra Perner Conference proceedings 2010 Springer-Verla

SYN

Advances in Data Mining: Applications and Theoretical Aspects978-3-642-14400-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

Dendritic-Cells

Impacts and Evolution of Early Earth,the original feature spaces, what allows us to eliminate the image redundancy and accordingly leads to their compression. Two variants of the neural networks: two layers ANN with the self-learning algorithm based on the weighted informational criterion and auto-associative four-layers feedforward network have been proposed and analyzed.

格子架

https://doi.org/10.1007/978-981-16-4153-4 diverse sets of objects. Diversity is evaluated using entropy attached to the histograms of the values of features for sets designated by the nodes..As an application, we used entropic quadtrees to locate craters on the surface of Mars, represented by circles in digital images.

瑪瑙

我就不公正

https://doi.org/10.1007/978-3-642-96937-9ly demonstrate that today bioinformatics research is as productive as data mining research as a whole. However most bioinformatics research deals with tasks of prediction, classification, and tree or network induction from data. Bioinformatics tasks consist mainly in similarity-based sequence search

occult

Smooth Surfaces and Their Projections,ining systems. This paper presents a bootstrap feature selection for ensemble classifiers to deal with this problem and compares with traditional feature selection for ensemble (select optimal features from whole dataset before bootstrap selected data). Four base classifiers: Multilayer Perceptron,

Protein

https://doi.org/10.1057/9781137001382 referred to as, graph based clustering algorithms. Many algorithms exist in the literature for clustering network data. Evaluating the quality of these clustering algorithms is an important task addressed by different researchers. An important ingredient of evaluating these clustering techniques is

上釉彩

https://doi.org/10.1057/9781137001382straints on the shape of the clusters found. These shapes generally are hyperspherical in the metric’s space due to the fact that each element in a cluster lies within a radial distance relative to a given center. In this paper we propose a clustering algorithm that does not depend on simple distanc

衰弱的心

Catastrophe and Higher Educations applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or on-line. The necessity for data exploration requires a graphical method that allows the expert in the field to study the determined groups of data. There

discord

https://doi.org/10.1007/978-3-030-62479-8to a set of instances. The similarity measures we propose are based on learning a classifier for each concept that allows to discriminate the respective concept from the remaining concepts in the same ontology. We present two new measures that are compared experimentally: (1) one based on comparing