"Data Engineering: Mining, Information and Intelligence"

EBook Description: It is quite clear that the world is awash in all kinds of data. In fact, the sheer volume of data adds little value to human activity and choices. The key to the vast amounts of data and information available to us is to distill and organize the data into information that we can productively use. Therefore, the most desirable information is that information which can be used to provide insight and intelligence for actionable strategies. DATA ENGINEERING: Mining, Information and Intelligence focuses specifically on applied information-warehousing and data-mining research that is being used or can be used by both academic researchers and industry enterprises. Moreover, the book will be the first to categorize and synthesize the diverse methodologies that are used in these interrelated fields into a structured approach entitled, Data Engineering

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered � this is the challenge created by today�s abundance of data.

Fundamentals of Data Mining in Genomics and Proteomics

This book aims to present state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. Research and development in genomics and proteomics depend on the analysis and interpretation of large amounts of data generated by high-throughput techniques. To exploit data obtained from experimental and observational studies, life scientists need to understand the analytical techniques and methods from statistics and data mining. These techniques are not easily accessible to life scientists working on genomics and proteomics problems, as the available material is presented from a highly mathematical perspective, favoring formal rigor over conceptual clarity and assessment of practical relevance. This book addresses these issues by adopting an approach focusing on concepts and applications. It presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Data Mining - Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Concepts, Models, Methods, and Algorithms

Data mining describes the often complex and sophisticated tools used in automatic data analysis such as analyzing a customer's previous buying habits
Emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning
Describes representative state-of-the-art methods and algorithms originating from different disciplines
Offers guidance on how and when to use a particular software tool from among the hundreds offered when faced with a data set to mine

Data Mining: Concepts, Models, Methods, and Algorithms

ata mining describes the often complex and sophisticated tools used in automatic data analysis such as analyzing a customer's previous buying habits

Emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning

Describes representative state-of-the-art methods and algorithms originating from different disciplines

Offers guidance on how and when to use a particular software tool from among the hundreds offered when faced with a data set to mine

"New Fundamental Technologies in Data Mining"

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by �Data Mining� address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data minin

"Exploratory Data Analysis Using Fisher Information"

The basic goal of a research scientist is to understand a given, unknown system. This innovative book develops a systematic approach for achieving this goal. All science is ultimately dependent upon observation which, in turn, requires a flow of information. Fisher information, in particular, is found to provide the key to understanding the system. It is developed as a new tool of exploratory data analysis, and is applied to a wide scope of systems problems. These range from molecules in a gas to biological organisms in their ecologies, to the socio-economic organization of people in their societies, to the physical constants in the universe and, ultimately, to proto-universes in the multiverse.

"New Perspectives in Statistical Modeling and Data Analysis

This volume provides recent research results in data analysis, classification and multivariate statistics and highlights perspectives for new scientific developments within these areas. Particular attention is devoted to methodological issues in clustering, statistical modeling and data mining. The volume also contains significant contributions to a wide range of applications such as finance, marketing, and social sciences. The papers in this volume were first presented at the 7th Conference of the Classification and Data Analysis Group (ClaDAG) of the Italian Statistical Society, held at the University of Catania, Italy.

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a host of different entities engaged in a wide variety of activities in many contexts worldwide. In many cases evaluations of these entities have been resistant to other approaches because complex, multiple levels of (often) poorly understood relations must be considered. A few examples of these multifaceted problems are (1) maintenance activities of US Air Force bases in geographically dispersed locations, (2) police force efficiencies in the United Kingdom, (3) branch bank performances in Canada, Cyprus, and other countries and (4) the efficiency of universities in performing their education and research functions in the U.S., England, and France. In addition to localized problems, DEA applications have been extended to performance evaluations of `larger entities' such as cities, regions, and countries.

Data analysis for hyphenated techniques

Based on a concrete set of working MATLAB programs, the book begins with a short program of snippets describing basic principles and proceeds to a complete application program filling a central analytical need: obtaining pure spectra from the observed overlapping spectra, with standard deviations for the solutions obtained.
The strength of the book is the open nature of the programs. All programs can be read, tested and modified by the user. The mathematical principles needed for the proper treatment of experimental data are thoroughly described. The first part of the book describes how data is collected, converted and prepared for the actual deconvolution. Practical working algorithms such as the Savitzky-Golay smoothing method are emphasized. The reader can see how searches in spectral libraries can be greatly speeded up by proper formulation of the calculation. Basic signal processing is described by illustrative examples.
The main part of the book describes the deconvolution of overlapping chromatographic peaks. Principle component analysis is described and used as a useful tool. The main emphasis is a discussion of a deconvolution method, OSCAR (Optimization by Stepwise Constraining of Alternating Regression), developed by the authors. The results can be validated as OSCAR calculates confidence intervals for the spectra and elution curves.

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