The main aim of this book is to offer an exposition of the principles and applications of an original method which was introduced by the authors, developed gradually in the course of time, and applied extensively in the most diverse fields of management in the mining industry and power engineering. It is a relatively universal method of mathematical model construction and application intended to aid managerial personnel at various management levels in decision-making situations, which are frequently characterized by complicated relations of a quantitative as well as logical character.The method, called by the authors simply the ``method of mathematical-logical modelling'' (MLM for short), is based upon an interesting and effective combination of tools from mathematical logic, Boolean algebra and computer programming. From the mathematical point of view it is based primarily on the construction and solution of systems of pseudo-Boolean equations and inequalities with a generalized logical structure. The principal features of the method are its universality, iterativity, interactivity, and advanced and broadly applicable software, coded in FORTRAN 77. Due in particular to these properties, MLM is a powerful tool for modelling real-life situations in the mining industry (and, naturally, in other fields of human activity as well).The exposition is illustrated by a considerable number of examples. Some of these are rather simple and aimed at helping the reader verify his correct understanding of the text. Other examples, especially in the second part of the book (Chapters 6, 7 and 8), are more complicated and extensive. In some instances they have the character of case studies and demonstrate typical approaches applied when modelling mining situations.The book will be of interest to a broad range of specialists working in the mining industry - research workers, designers, computer personnel, system analysts, management personnel at all managerial levels, and also undergraduate as well as graduate students.

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. · Teaches how to discover, construct and refine models that are useful in business situations· Teaches how to design, discover and develop the data necessary for mining · Provides a practical approach to mining data for all business situations· Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data· Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.

Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

Worldwide demand for sand and gravel is increasing daily, as the need for these materials continues to rise, for example in the construction sector, in land filling and for transportation sector based infrastructural projects. This results in over-extraction of sand from channel beds, and hampers the natural renewal of sediment, geological setup and morphological processes of the riverine system. In India, illegal sand mining (of alluvial channels) and gravel mining (of perennial channels) are two anthropogenic issues that negatively affect the sustainable drainage system. Along the Kangsabati River in India, the consequences of sand mining are very serious. The construction of Mukutmonipur Dam (1958) on the river causes huge sediment deposition along the middle and downstream areas, these same areas are also intensely mined for sand (instream and on the flood plain). Geospatial models are applied in order to better understand the state and the resilience of stream hydraulics, morphological and river ecosystem variables during pre-mining and post-mining stages, using micro-level datasets of the Kangsabati River. The book also includes practicable measures to minimize the environmental consequences of instream mining in respect to optimum sand mining. It discusses the threshold limits of each variable in stream hydraulics, morphological and river ecological regime, and also discusses the most affected variables. Consequently, all outputs will be very useful for students, researchers, academicians, decision makers and practitioners and will facilitate applying these techniques to create models for other river basins.

This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: [email protected]

This book introduces the concept of Event Mining for building explanatory models from analyses of correlated data. Such a model may be used as the basis for predictions and corrective actions. The idea is to create, via an iterative process, a model that explains causal relationships in the form of structural and temporal patterns in the data. The first phase is the data-driven process of hypothesis formation, requiring the analysis of large amounts of data to find strong candidate hypotheses. The second phase is hypothesis testing, wherein a domain expert’s knowledge and judgment is used to test and modify the candidate hypotheses. The book is intended as a primer on Event Mining for data-enthusiasts and information professionals interested in employing these event-based data analysis techniques in diverse applications. The reader is introduced to frameworks for temporal knowledge representation and reasoning, as well as temporal data mining and pattern discovery. Also discussed are the design principles of event mining systems. The approach is reified by the presentation of an event mining system called EventMiner, a computational framework for building explanatory models. The book contains case studies of using EventMiner in asthma risk management and an architecture for the objective self. The text can be used by researchers interested in harnessing the value of heterogeneous big data for designing explanatory event-based models in diverse application areas such as healthcare, biological data analytics, predictive maintenance of systems, computer networks, and business intelligence.

Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.