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

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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’

In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this paper proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely, the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation, and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. The Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the PIs. To demonstrate the performance of the DPO method, the practical data coming from the energy center of a steel plant are employed. The results show that the proposed DPO method can supply the human operators with effective solution for secure and economically justified optimization of the gas system. Note to Practitioners —Given that the byproduct gas system in steel industry can hardly be described by a physics or mechanism-based model, its operation is widely realized by the experience-based manual measure at present, which exhibits a very low automation level. Since a large number of real-time energy data have been accumulated by the existing SCADA system implemented in most of steel plants, a novel data-driven real-time predictive optimization method is proposed in this study. The proposed method aims at the short term energy optimization, thus the sample interval of the real-time data acquired from the SCADA system is set as 1 minute. The application system can provide the rolling optimized solution via real-time predicting the running circumstances of the gas system. Therefore, it is required for the plant in advance to implement the SCADA system for the energy data acquisition, and the sampling interval should be less than or equal to 1 minute. Furthermore, it is necessary for the sample data to complete the preliminary processing such as data imputation if needed since there are usually a large number of possible missing data points existed in the SCADA system of the production practice. Because such preliminary processing for the sample data belongs to a class of generic methods, this study avoids the redundant technical introduction.

Data-Based Predictive Optimization for Byproduct Gas System in Steel Industry

Knowledge-based networks in classification problems

Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.

A Gradient-Descent-Based Approach for Transparent Linguistic Interface Generation in Fuzzy Models

Advances in Kernel Methods. Support Vector Learning, B. Scholkopf, C.J.C. Burges, A.J. Smola (Eds.); MIT Press, Cambridge, 1999, pp. 376+vii. ISBN 0-262-19416-3.

In this paper we discuss the issue of granular representation of time series. The critical concern is the ability to capture the essential features of the time series in the abstract granular representation of it. The discussion uses a set-theoretical framework of fuzzy sets and employs the Fuzzy C-means algorithm for the evaluation of the information granules obtained in various ways.

Witold Pedrycz

Papers

Data-Based Predictive Optimization for Byproduct Gas System in Steel Industry

Knowledge-based networks in classification problems

A Gradient-Descent-Based Approach for Transparent Linguistic Interface Generation in Fuzzy Models

Advances in Kernel Methods. Support Vector Learning, B. Scholkopf, C.J.C. Burges, A.J. Smola (Eds.); MIT Press, Cambridge, 1999, pp. 376+vii. ISBN 0-262-19416-3.

Granulation of temporal data: a global view on time series