Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic
Старое издание
Автор: Timothy Masters Название: Data Mining Algorithms in C++ ISBN: 148423314X ISBN-13(EAN): 9781484233146 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What you'll learn
Monte-Carlo permutation tests provide statistically sound assessment of relationships present in your data.
Combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data.
Feature weighting as regularized energy-based learning ranks variables according to their predictive power when there is too little data for traditional methods.
The eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data.
Plotting regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high, provides visual insight into anomalous relationships.
Who this book is for
The techniques presented in this book and in the DATAMINE program will be useful to anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
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.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 9720.00 р. Наличие на складе: Поставка под заказ.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Автор: Voulgaris Zacharias Название: Julia for Data Science ISBN: 1634621301 ISBN-13(EAN): 9781634621304 Издательство: Gazelle Book Services Рейтинг: Цена: 6200.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.
Автор: Andrea Tagarelli Название: XML Data Mining: Models, Methods, and Applications ISBN: 1613503563 ISBN-13(EAN): 9781613503560 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.
Автор: С.Aggarwal Название: Data Mining: The Textbook ISBN: 3319141414 ISBN-13(EAN): 9783319141411 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Поставка под заказ.
Описание: This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
Автор: Klein Название: Basic Concepts in Data Structures ISBN: 1316613844 ISBN-13(EAN): 9781316613849 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An easy-to-read guide on basic concepts of data structures, this textbook covers the theoretical side to the art of writing computer programs. Designed for undergraduates in any quantitative field, the book covers all the most common data structures.
Автор: Philip S. Yu; Jiawei Han; Christos Faloutsos Название: Link Mining: Models, Algorithms, and Applications ISBN: 1493901478 ISBN-13(EAN): 9781493901470 Издательство: Springer Рейтинг: Цена: 28732.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Andrade Название: Fundamentals of Stream Processing ISBN: 1107015545 ISBN-13(EAN): 9781107015548 Издательство: Cambridge Academ Рейтинг: Цена: 13781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book teaches fundamentals of the stream processing paradigm that addresses performance, scalability and usability challenges in extracting insights from massive amounts of live, streaming data. It presents core principles behind application design, system infrastructure and analytics, coupled with real-world examples for a comprehensive understanding of the stream processing area.
Автор: Gisele L. Pappa; Alex Freitas Название: Automating the Design of Data Mining Algorithms ISBN: 3642261256 ISBN-13(EAN): 9783642261251 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This unique text seeks to automate the design of a data mining algorithm. It first overviews data mining and evolutionary algorithms then discusses the design of a new genetic programming system for automating the design of full rule induction algorithms.
Автор: Pawel Cichosz Название: Data Mining Algorithms: Explained Using R ISBN: 111833258X ISBN-13(EAN): 9781118332580 Издательство: Wiley Рейтинг: Цена: 9971.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles.
Автор: Onwubolu Godfrey C Название: Gmdh-Methodology And Implementation In Matlab ISBN: 1783266120 ISBN-13(EAN): 9781783266128 Издательство: World Scientific Publishing Рейтинг: Цена: 14256.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization.
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