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

Summary

In this work, we focus on two main themes: (i) developing a more realistic macroscopic fundamental diagram-based nonlinear control-oriented model of urban traffic networks with time delays incorporated into model structure; and (ii) based on its linearized form subject to input delay, designing two new perimeter control architectures for one aggregated urban traffic region under unknown bounded external disturbances and parameter uncertainties. The feedback control laws design is performed in the context of model reference adaptive control approach. Simulation results based on a linearized model are also presented, which demonstrate in this case desired closed-loop adaptive control system performance. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive perimeter traffic control of urban road networks based on MFD model with time delays

Perimeter traffic control for large-scale urban road networks has been studied by several researchers during the last decade. Recently, the initial steps towards taking into account model’s uncertainties under control synthesis were made in Haddad and Shraiber (2014) and Haddad (2015), where by considering one- and multi- region control problem, respectively, a robust perimeter control has been designed to systematically take into account uncertainties in MFD-based dynamics, e.g. the MFD scatter. The robust control design can provide a fixed controller with constant gains to compensate all uncertainties, following the worst case scenario concept. In this paper, an adaptive control scheme is developed. Similarly to robust control, the developed adaptive control scheme postulates one controller structure, however, the controllers’ gains vary with time to adapt themselves against the model parameter uncertainties. In this paper, in order to accommodate uncertainties and take into consideration the restrictions on the available information, we deal with the adaptive perimeter control problem for multi-region MFD systems, which have an interconnected structure composing several homogeneous regions. Unlike previous works that assume centralized approach, where feedback informations are needed from all urban regions, in this paper we follow a coordinated distributed control approach, where regional control laws are developed depending on (i) real on-line local information of the region, i.e. regional accumulation and its perimeter control input only, and (ii) reference signal information forwarded to all distributed perimeter controllers by a high level coordinator controller.

Coordinated distributed adaptive perimeter control for large-scale urban road networks

https://www.dcs.bbk.ac.uk/~mirkin/papers/is05.pdf

Nearest neighbour approach in the least-squares data imputation algorithms

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Mathematical Hierarchies and Biology

We review methods of qualitative factor analysis (QFA) developed by the author and his collaborators over the last decade and discuss the use of QFA methods for the additive clustering problem. The QFA method includes, first, finding a square Boolean matrix in a fixed set of Boolean matrices with “simple structures” to approximate a given similarity matrix, and, second, repeating this process again and again using residual similarity matrices. We present convergence properties for three versions of the method, provide “cluster” interpretations for results obtained from the algorithms, and give formulas for the evaluation of “factor shares” of the initial similarities variance.

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Boris Mirkin

Papers

Adaptive perimeter traffic control of urban road networks based on MFD model with time delays

Coordinated distributed adaptive perimeter control for large-scale urban road networks

Nearest neighbour approach in the least-squares data imputation algorithms

Mathematical Hierarchies and Biology

Additive clustering and qualitative factor analysis methods for similarity matrices