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Principles of Data Mining

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas.

Learn Data Mining Through Excel

A Step-by-Step Approach for Understanding Machine Learning Methods

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel.

Data Mining and Business Analytics with R

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: • A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools • Illustrations of how to use the outlined concepts in real-world situations • Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials • Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional ...

Data Science for Business

What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

This guide also helps you understand the many data-mining techniques in use today.

Data Mining

3. Auflage

Data Mining liefert Methoden und Algorithmen, um bisher unbekannte Zusammenhänge bei großen Datenmengen zu entdecken.Das Buch deckt den Stoff einer einsemestrigen Vorlesung an Universitäten oder Fachhochschulen ab und ist als klassisches Lehrbuch konzipiert. Es bietet Zusammenfassungen, zahlreiche Beispiele und Übungsaufgaben. Die dritte Auflage bettet die Themen ein in ASpekte wie KI, Big Data, Data Science, Machine Learning.

Data Mining liefert Methoden und Algorithmen, um bisher unbekannte Zusammenhänge bei großen Datenmengen zu entdecken.Das Buch deckt den Stoff einer einsemestrigen Vorlesung an Universitäten oder Fachhochschulen ab und ist als klassisches ...

Data Mining

Practical Machine Learning Tools and Techniques

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 http://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

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 ...

Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book.

SISTEM INFORMASI MANAJEMEN

Sistem Informasi Manajemen (SIM) merupakan sistem perencanaan bagian dari pengendalian internal suatu bisnis yang meliputi pemanfaatan manusia, dokumen, teknologi, dan prosedur oleh akuntansi manajemen untuk memecahkan masalah bisnis seperti biaya produk, layanan, atau suatu strategi bisnis, sebagai sistem informasi yang digunakan untuk mengambil keputusan, mengkoordinasi, mengontrol, menganalisis, serta memvisualisasi suatu informasi dalam organisasi. Sistem Informasi Manajemen ini terdiri dari hardware maupun software yang berfungsi sebagai dasar operasi suatu organisasi. SIM bekerja dengan cara mengumpulkan data-data dari beberapa sistem online untuk dianalisis, kemudian SIM akan melaporkan hasil analisis tersebut membantu manajemen mengambil keputusan, membuat perencanaan, atau memecahkan suatu masalah. Buku ini akan menbantu para pelaku usaha dalam memajukan dan mengembangkan bisnisnya, sehingga buku ini harus dimiliki oleh khalayak. Maka dari itu buku ini hadir kehadapan sidang pembaca sebagai bagian dari upaya diskusi sekaligus dalam rangka melengkapi khazanah keilmuan dibidang teknologi informasi, sehingga buku ini sangat cocok untuk dijadikan bahan acuan bagi kalangan intelektual dilingkungan perguruan tinggi ataupun praktisi yang berkecimpung langsung dibidang teknologi informasi.

Sistem Informasi Manajemen (SIM) merupakan sistem perencanaan bagian dari pengendalian internal suatu bisnis yang meliputi pemanfaatan manusia, dokumen, teknologi, dan prosedur oleh akuntansi manajemen untuk memecahkan masalah bisnis ...

SISTEM INFORMASI MANAJEMEN

Seiring dengan perkembangan teknologi dalam dunia bisnis, sistem informasi manajemen sangat dibutuhkan untuk membantu kegiatan bisnis agar tetap bisa berjalan dengan baik. Hampir di setiap bidang pasti membutuhkan sebuah sistem yang dapat mengontrol dan mengurus sebuah informasi dengan baik dan rapi. Sistem informasi manajemen atau SIM adalah sistem perencanaan bagian dari pengendalian internal suatu bisnis yang meliputi pemanfaatan manusia, dokumen, teknologi, dan prosedur oleh akuntansi manajemen untuk memecahkan masalah bisnis seperti biaya produk, layanan, atau suatu strategi bisnis. Sistem informasi manajemen (SIM) adalah sekelompok atau sekumpulan proses dimana data dapat diolah, dianalisis, dan ditampilkan supaya data tersebut menjadi berguna untuk kebutuhan pengambilan suatu keputusan. Sistem ini merupakan alat yang sangat berguna untuk menunjang dan mengendalikan operasi perusahaan. Tujuan utama dari sistem ini adalah untuk mengumpulkan dan mengatur semua data dari berbagai tingkat perusahaan, meringkas, kemudian memfasilitasi dan meningkatkan kualitas dari pengambilan keputusan untuk meningkatkan produktivitas dan profitabilitas sebuah perusahaan. Sistem ini berbasis komputer dan dapat berupa lembar excel atau platform yang lebih kompleks. Selain itu data dapat diakses dan diolah secara internal maupun eksternal. Sehingga, sistem informasi yang digunakan lebih efisien dan produktif. Buku ini menyajikan seluruh kebutuhan-kebutuhan para pelaku usaha sebagai inovasi baru untuk menciptakan tatanan pengelolaan, juga menjadikan gudang wawasan bagai kalangan pembaca. Oleh sebab itu buku ini hadir kehadapan sidang pembaca sebagai bagian dari upaya diskusi sekaligus dalam rangka melengkapi khazanah keilmuan dibidang manajemen sistem informasi, sehingga buku ini sangat cocok untuk dijadikan bahan acuan bagi kalangan intelektual dilingkungan perguruan tinggi ataupun praktisi yang berkecimpung langsung dibidang manajemen sistem informasi.

Sistem informasi manajemen atau SIM adalah sistem perencanaan bagian dari pengendalian internal suatu bisnis yang meliputi pemanfaatan manusia, dokumen, teknologi, dan prosedur oleh akuntansi manajemen untuk memecahkan masalah bisnis ...

Sistem Informasi Manajemen

Dewasa ini, informasi menjadi sumber daya yang penting sejalan dengan perkembangan teknologi informasi dan komputer. Buku ini disajikan secara ringkas dan menggunakan bahasa Indonesia yang mudah dipahami bagi para mahasiswa dan pembaca yang berminat. Buku ini membahas konsep-konsep, teori-teori dan disajikan pula praktik-praktik sistem informasi terkait dengan praktik manajemen bisnis modem. Buku ini ditulis oleh Dr. Achmad Sudiro, SE., ME., Perdana Rahadhan, SE., dan Ir. Nur Prima, MM., Tim Dosen pengajar Sistem Informasi Manajemen pada jurusan Manajemen Fakultas Ekonomi Universitas Brawijaya.

Buku ini ditulis oleh Dr. Achmad Sudiro, SE., ME., Perdana Rahadhan, SE., dan Ir. Nur Prima, MM., Tim Dosen pengajar Sistem Informasi Manajemen pada jurusan Manajemen Fakultas Ekonomi Universitas Brawijaya.