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

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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

Proteins are the fundamental molecules of life, they form both the structural and the functional building blocks of cells in all living organisms. Proteins are also the most complex molecules. Their three-dimensional structures are extremely diverse, and so are their biological functions. Elucidation of the relationship between protein sequence, structure, and function is one of the most significant open problems in science and technology. Despite the growing body of observed data and partial theories, there are still no satisfactory methods which would enable us to analyze, characterize, and predict a protein's structure and/or function with regard to its known primary sequence. There are two fundamentally different approaches to the problem, and a continuum of their combinations. The first approach is based on theory and models from physics and chemistry, the other is based on computational and statistical analysis of observed data. This paper belongs to the second direction. From the data analysis perspective, we can observe, first, that there has not been much achieved in terms of producing reasonable feature spaces except for those directly related to amino acid sequence similarity data, and, second, the specifics of emerging problems yet have not been addressed in full. One of the specific issues is related to difference between prediction and description. Typically, prediction is based on a description of the phenomenon in question in the framework of a prediction model. However,

A Feature-Based Approach to Discrimination and Prediction of Protein Folding

Early developments in data analysis in Russia, and in the Soviet Union in general have been well separated by the rather tight iron curtain through all of the covered period. However, they generally followed the international developments, because some monographs were translated into Russian, best journals reached a few libraries with a lag of one-two years, and a few Soviet scientists did attend some conferences: the international framework distinguishing between factor analysis, pattern recognition and clustering prevailed. Yet there have been a number of indigenous developments of which by far the most popular has become the structural risk minimization by V. Vapnik and Chervonenkis (Vapnik and Chervonenkis 1974) along with the concept of VC-complexity and the method of generalized portrait (Vapnik and Lerner1963) later, in 90-es, extended to the renowned Support Vector Machine (SVM) with nonlinear kernels – the latter was proposed as a tool for supervised and unsupervised learning by Bashkirov, Braverman and Muchnik (1964) under the name of “potential functions”; for further developments, mostly extending the perceptron framework, see Aizerman, Braverman, and Rozonoer (1970). Much less known are the following contributions: M. Bongard proposed logic predicate data analysis involving invented by him benchmark examples (Bongard 1967) that have become known as Bongard problems in cognitive psychology (see, for example, Linhares (2000)); S. Chesnokov developed his determinacy analysis based on conditional probabilities to anticipate the analysis of associations in data mining (Chesnokov 1982); V. Fain proposed using techniques of the continuous group theory for describing image geometry changes (Fain 1970);

Journ@l Electronique d'Histoire des Probabilités et de la Statistique Electronic Journ@l for History of Probability and Statistics

In this paper, we develop a tube based model reference adaptive configuration for a class of nonlinear dynamic systems with actuator failure which not only guarantees closed loop stability, asymptotically exact tracking and robustness to a nonlinear perturbation, and to an external disturbance with unknown bounds, but also diminishes the control cost with respect to some criterion.

Fault tolerant tube-based adaptive tracking for a class of nonlinear plants

We describe a novel method for the analysis of research activities of an organization by mapping that to a taxonomy tree of the field. The method constructs fuzzy membership profiles of the organizationmembers or teams in terms of the taxonomy’s leaves (research topics), and then it generalizes them in two steps. These steps are: (i) fuzzy clustering research topics according to their thematic similarities in the department, ignoring the topology of the taxonomy, and (ii) optimally lifting clusters mapped to the taxonomy tree to higher ranked categories by ignoring “small” discrepancies. We illustrate the method by applying it to data collected by using an in-house e-survey tool from a university department and from a university research center. The method can be considered for knowledge generalization over any taxonomy tree.

Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of Its Field

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Boris Mirkin

Papers

A Feature-Based Approach to Discrimination and Prediction of Protein Folding

Journ@l Electronique d'Histoire des Probabilités et de la Statistique Electronic Journ@l for History of Probability and Statistics

Fault tolerant tube-based adaptive tracking for a class of nonlinear plants

Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of Its Field

Detection of an Unspecified Number of Communities in Feature-Rich Networks