Articles by "Data Mining"

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Showing posts with label Data Mining. Show all posts

David L. Olson, Desheng Wu ... 102 pages - Publisher: Springer; (September, 26) ... Language: English - ASIN: B01LWORTEH by Amazon ...

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.

Daniel J. Denis ... 760 pages - Publisher: Wiley; (December, 2015) ... Language: English - ISBN-10: 9781118632338 - ISBN-13: 978-1118632338 ...

A clear and efficient balance between theory and application of statistical modeling techniques in the social and behavioral sciences: Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences. Blending statistical theory and methodology, the book surveys both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques such as t-tests and correlation as well as more advanced procedures such as MANOVA, factor analysis, and structural equation modeling. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. Applied Univariate, Bivariate, and Multivariate Statistics also features: Demonstrations of statistical techniques using software packages such as R and SPSS + Examples of hypothetical and real data with subsequent statistical analyses + Historical and philosophical insights into many of the techniques used in modern social science + A companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options. An ideal textbook for courses in statistics and methodology at the upper- undergraduate and graduate-levels in psychology, political science, biology, sociology, education, economics, communications, law, and survey research, Applied Univariate, Bivariate, and Multivariate Statistics is also a useful reference for practitioners and researchers in their field of application.

January 26, 2019 , ,
David L. Olson ... 173 pages - Publisher: Business Expert Press; (June, 2016) ... Language: English - ISBN-10: 9781631575488 - ISBN-13: 978-1631575488 ...

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Johan A. K. Suykens, Marco Signoretto ... 525 pages - Publisher: Chapman and Hall/CRC; (October, 2014) ... Language: English - ISBN-10: 1482241390 - ISBN-13: 978-1482241396 ...

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso * Discusses multi-layer SVMs * Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing * Describes graph-based regularization methods for single- and multi-task learning * Considers regularized methods for dictionary learning and portfolio selection * Addresses non-negative matrix factorization * Examines low-rank matrix and tensor-based models * Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing * Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent. Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Jian Qing Shi, Taeryon Choi ... 216 pages - Publisher: CRC; (July, 2011) .. Language: English - ISBN-10: 1439837732 - ISBN-13: 978-1439837733 ...

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author’s website.

Samprit Chatterjee, Ali S. Hadi ... 424 pages - Publisher: Wiley; 5th edition (September, 2012) ... Language: English - ISBN-10: 0470905840 - ISBN-13: 978-0470905845...

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however,requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics,transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression * Fitting nonlinear models * Errors in variables * ANOVA for designed experiments. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique.Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

September 17, 2018 , ,
Robert Nisbet, Gary Miner, Ken Yale ... 822 pages - Publisher: Academic Press; 2nd edition (November, 2017) ... Language: English - ISBN-10: 0124166326 - ISBN-13: 978-0124166325 ...

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas―from science and engineering, to medicine, academia and commerce. * Includes input by practitioners for practitioners * Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models * Contains practical advice from successful real-world implementations * Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions * Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications.

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal ... 655 pages - Publisher: Morgan Kaufmann; 4th edition (October, 2016) ... Language: English - ASIN: B01MG31RL3 by Amazon ...

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

September 08, 2016 , ,
Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis ... 59 pages - Publisher: Springer New York; (November, 2012) ... Language: English - ASIN: B00AYR26DI by Amazon

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.

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