Download or read book MASTERING DATA MINING THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT written by Michael J. A. Berry and published by . This book was released on 2008-09-01 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers.

Download or read book Mastering Data Mining written by Michael J. A. Berry and published by Wiley. This book was released on 1999-12-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Berry and Linoff lead the reader down an enlightened path of best practices." -Dr. Jim Goodnight, President and Cofounder, SAS Institute Inc. "This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit Mastering Data Mining In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management. In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications. Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries. Berry and Linoff show you how to use data mining to:Retain customer loyaltyTarget the right prospectsIdentify new markets for products and servicesRecognize cross-selling opportunities on and off the Web The companion Web site at http://www.data-miners.com features:Updated information on data mining products and service providersInformation on data mining conferences, courses, and other sources of informationFull-color versions of the illustrations used in the book.

Download or read book Mastering Data Mining written by Berry and published by . This book was released on 2000 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Data Mining written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncover Hidden Insights and Patterns in Your Data Are you ready to delve into the fascinating realm of data mining? "Mastering Data Mining" is your ultimate guide to unlocking the potential of extracting hidden insights and patterns from your data. Whether you're a data scientist aiming to uncover valuable information or a business professional seeking to make informed decisions, this book equips you with the knowledge and techniques to master the art of data mining. Key Features: 1. Journey into Data Mining: Immerse yourself in the world of data mining, understanding its significance, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Preparation: Master the art of data exploration and preparation. Learn how to clean, transform, and preprocess data for effective mining. 3. Exploratory Data Analysis: Delve into exploratory data analysis techniques. Explore visualization, statistical summaries, and data profiling to gain a deeper understanding of your dataset. 4. Supervised Learning Techniques: Uncover the power of supervised learning techniques. Learn how to build predictive models for classification and regression tasks, enabling you to make accurate predictions. 5. Unsupervised Learning and Clustering: Discover unsupervised learning and clustering methods. Explore techniques for grouping similar data points and identifying hidden patterns without predefined labels. 6. Association Rule Mining: Master association rule mining for uncovering relationships in data. Learn how to identify frequent itemsets and extract valuable associations. 7. Text and Web Mining: Explore text and web mining techniques. Learn how to extract insights from textual data and discover patterns in web-based information. 8. Time Series Mining: Delve into time series mining for analyzing sequential data. Learn how to forecast trends, identify anomalies, and make predictions based on temporal patterns. 9. Data Mining Tools and Algorithms: Uncover a range of data mining tools and algorithms. Explore classic algorithms and modern techniques for various data mining tasks. 10. Real-World Applications: Gain insights into real-world use cases of data mining across industries. From customer segmentation to fraud detection, explore how organizations leverage data mining for strategic advantage. Who This Book Is For: "Mastering Data Mining" is an indispensable resource for data scientists, analysts, and business professionals who want to excel in uncovering insights from data. Whether you're new to data mining or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data mining. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Download or read book Data Mining Techniques written by Michael J. A. Berry and published by John Wiley & Sons. This book was released on 2004-04-09 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Download or read book Mastering Data Mining the Art and Science of Customer Relationship Management written by Michael J. A. Berry and published by . This book was released on 2000 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining Concepts and Techniques written by Jiawei Han and published by Elsevier. This book was released on 2011-06-09 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Download or read book Data Mining written by Ian H. Witten and published by Elsevier. This book was released on 2011-02-03 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 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. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. 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

Download or read book Data Mining Techniques with Mastering Data Mining Set written by Michael J. A. Berry and published by Wiley. This book was released on 2003-05-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Social Media Mining with Python written by Marco Bonzanini and published by Packt Publishing Ltd. This book was released on 2016-07-29 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data Who This Book Is For This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data. What You Will Learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products In Detail Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. Style and approach This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.

Download or read book Mastering Data Mining with Python Find patterns hidden in your data written by Megan Squire and published by Packt Publishing Ltd. This book was released on 2016-08-29 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques About This Book Dive deeper into data mining with Python – don't be complacent, sharpen your skills! From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries Who This Book Is For This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you! What You Will Learn Explore techniques for finding frequent itemsets and association rules in large data sets Learn identification methods for entity matches across many different types of data Identify the basics of network mining and how to apply it to real-world data sets Discover methods for detecting the sentiment of text and for locating named entities in text Observe multiple techniques for automatically extracting summaries and generating topic models for text See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set In Detail Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics. Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.

Download or read book Mastering Data Analysis with R written by Gergely Daroczi and published by Packt Publishing Ltd. This book was released on 2015-09-30 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Download or read book Mastering Java for Data Science written by Alexey Grigorev and published by Packt Publishing Ltd. This book was released on 2017-04-27 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.

Download or read book Mastering Python for Data Science written by Samir Madhavan and published by Packt Publishing Ltd. This book was released on 2015-08-31 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Download or read book Mastering Data Mining with Python Find Patterns Hidden in Your Data written by Megan Squire and published by . This book was released on 2016-08-29 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniquesAbout This Book- Dive deeper into data mining with Python - don't be complacent, sharpen your skills!- From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge- Become a more fluent and confident Python data-analyst, in full control of its extensive range of librariesWho This Book Is ForThis book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you!What You Will Learn - Explore techniques for finding frequent itemsets and association rules in large data sets- Learn identification methods for entity matches across many different types of data- Identify the basics of network mining and how to apply it to real-world data sets- Discover methods for detecting the sentiment of text and for locating named entities in text- Observe multiple techniques for automatically extracting summaries and generating topic models for text- See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set In DetailData mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy - without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding.If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries.In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get.By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.

Download or read book Mastering Data Mining MS with Data Mining Set written by Michael J. A. Berry and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Text Mining with R written by Ashish Kumar and published by Packt Publishing Ltd. This book was released on 2016-12-28 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.