There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

ANFIS: adaptive-network-based fuzzy inference system

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

An identification algorithm in fuzzy relational systems

1: Introductory Remarks on Fuzzy Sets.- 2: Fuzzy Relation Equations in Residuated Lattices.- 3: Lower Solutions of Max-Min Fuzzy Equations.- 4: Measures of Fuzziness of Solutions of Max-Min Fuzzy Relation Equations on Linear Lattices.- 5: Boolean Solutions of Max-Min Fuzzy Equations.- 6: ?-Fuzzy Relation Equations and Decomposable Fuzzy Relations.- 7: Max-Min Decomposition Problem of a Fuzzy Relation in Linear Lattices.- 8: Fuzzy Relation Equations with Lower and Upper Semicontinuous Triangular Norms.- 9: Fuzzy Relation Equations with Equality and Difference Composition Operators.- 10: Approximate Solutions of Fuzzy Relation Equations.- 11: Handling Fuzziness in Knowledge-Based Systems.- 12: Construction of Knowledge Base, Its Validation and Optimization.- 13: Inference Algorithms in Knowledge-Based Systems.- 14: A Fuzzy Controller and Its Realization.- 15: Bibliographies.- Author Index.

Witold Pedrycz

Papers

An identification algorithm in fuzzy relational systems

Fuzzy Relation Equations and Their Applications to Knowledge Engineering

An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment

Fuzzy fractal dimensions and fuzzy modeling

Genetic learning of fuzzy cognitive maps