Rudolf Kruse

Bio: Rudolf Kruse is an academic researcher from Otto-von-Guericke University Magdeburg. The author has contributed to research in topics: Fuzzy logic & Fuzzy classification. The author has an hindex of 49, co-authored 475 publications receiving 12902 citations. Previous affiliations of Rudolf Kruse include University of Düsseldorf & Braunschweig University of Technology.

Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition

Abstract: Introduction. Basic Concepts. Classical Fuzzy Clustering Algorithms. Linear and Ellipsoidal Prototypes Shell Prototypes. Polygonal Object Boundaries. Cluster Estimation Models. Cluster Validity. Rule Generation with Clustering. Appendix. Bibliography.

Foundations of neuro-fuzzy systems

Abstract: From the Publisher: Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.

Symbolic and quantitative approaches to reasoning with uncertainty

Abstract: Symbolic and quantitative approaches to reasoning with uncertainty , Symbolic and quantitative approaches to reasoning with uncertainty , کتابخانه دیجیتال جندی شاپور اهواز

Statistics with vague data

Abstract: 1 Introduction.- 2 Vague Data.- 3 Fuzzy Sets of the Real Line.- 4 Operations on Fuzzy Sets.- 4.1 Set Theoretical Operations.- 4.2 On Zadeh's Extension Principle.- 4.3 Arithmetic Operations.- 5 Representation of Vague Data in a Digital Computer.- 6 Topological Properties of Fuzzy Set Spaces.- 7 Random Sets and Fuzzy Random Variables.- 8 Descriptive Statistics with Vague Data.- 8.1 Expected Value.- 8.2 Variance.- 8.3 Empirical Distribution Function.- 9 Distribution Functions and i.i.d.-Sequences of Random Variables.- 10 Limit Theorems.- 10.1 Strong Law of Large Numbers.- 10.2 Consistent Estimators in the Finite Case.- 10.3 Gliwenko-Cantelli Theorem.- 10.4 Related Results.- 11 Some Aspects of Statistical Inference.- 11.1 Parametric Point Estimation.- 11.2 Confidence Estimation.- 11.3 The Testing of Hypotheses.- 12 On a Software Tool for Statistics with Vague Data.- 12.1 Linguistic Modelling.- 12.2 Linguistic Approximation.- 12.3 Examples.- References.- List of Symbols.

Foundations of Fuzzy Systems

Abstract: From the Publisher: The strength of this book lies in its clear and precise examination of the theory of fuzzy systems. A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications. The authors highlight two important application areas: approximate reasoning in knowledge-based systems, and fuzzy control. Reflecting the state of the art in fuzzy systems research, the book is both comprehensive and practical in its approach. Its illustration of key concepts is based on a detailed analysis of the underlying semantics. Each chapter is enhanced by useful historical notes and extensive references. The book presents several industrial case studies and exercises designed to increase its appeal to advanced students and researchers in computer science, applied mathematics and engineering.

Data Mining: Concepts and Techniques

Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Survey of clustering algorithms

Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

Clustering by fast search and find of density peaks

Abstract: Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. We demonstrate the power of the algorithm on several test cases.