Books: 38

Data Mining

CoverTitleYear
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, more » the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. « less
2016
Concepts, Techniques, and Applications with JMP Pro
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a more » theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: * Detailed summaries that supply an outline of key topics at the beginning of each chapter * End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material * Data-rich case studies to illustrate various applications of data mining techniques * A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. « less
2016
Explore the world of data science from scratch with Julia by your side
KEY FEATURES * An in-depth exploration of Julia's growing ecosystem of packages * Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data more » sets BOOK DESCRIPTION Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. WHAT YOU WILL LEARN * Apply statistical models in Julia for data-driven decisions * Understanding the process of data munging and data preparation using Julia * Explore techniques to visualize data using Julia and D3 based packages * Using Julia to create self-learning systems using cutting edge machine learning algorithms * Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models * Build a recommendation engine in Julia * Dive into Julia’s deep learning framework and build a system using Mocha.jl ABOUT THE AUTHOR Anshul Joshi is a data science professional with more than 2 years of experience primarily in data munging, recommendation systems, predictive modeling, and distributed computing. He is a deep learning and AI enthusiast. Most of the time, he can be caught exploring GitHub or trying anything new on which he can get his hands on. He blogs on anshuljoshi.xyz. TABLE OF CONTENTS 1. The Groundwork – Julia's Environment 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning « less
2016
Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques
KEY FEATURES * 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 more » of its extensive range of libraries BOOK DESCRIPTION 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. 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 ABOUT THE AUTHOR Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects. TABLE OF CONTENTS 1. Expanding Your Data Mining Toolbox 2. Association Rule Mining 3. Entity Matching 4. Network Analysis 5. Sentiment Analysis in Text 6. Named Entity Recognition in Text 7. Automatic Text Summarization 8. Topic Modeling in Text 9. Mining for Data Anomalies « less
2016
Learn about data mining with real-world datasets
ABOUT THIS BOOK * Diverse real-world datasets to teach data mining techniques * Practical and focused on real-world data mining cases, this book covers concepts such as spatial data mining, text mining, social media mining, and web mining * Real-world case studies illustrate various data mining techniques, more » taking you from novice to intermediate WHO THIS BOOK IS FOR Data analysts from beginner to intermediate level who need a step-by-step helping hand in developing complex data mining projects are the ideal audience for this book. They should have prior knowledge of basic statistics and little bit of programming language experience in any tool or platform. WHAT YOU WILL LEARN * Make use of statistics and programming to learn data mining concepts and its applications * Use R Programming to apply statistical models on data * Create predictive models to be applied for performing classification, prediction and recommendation * Use of various libraries available on R CRAN (comprehensive R archives network) in data mining * Apply data management steps in handling large datasets * Learn various data visualization libraries available in R for representing data * Implement various dimension reduction techniques to handle large datasets * Acquire knowledge about neural network concept drawn from computer science and its applications in data mining IN DETAIL The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users. This book explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects. STYLE AND APPROACH This fast-paced guide will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases. « less
2016
Harness the power of Python to analyze data and create insightful predictive models
ABOUT THIS BOOK * Learn data mining in practical terms, using a wide variety of libraries and techniques * Learn how to find, manipulate, and analyze data using Python * Step-by-step instructions on creating real-world applications of data mining techniques WHO THIS BOOK IS FOR If you are a programmer more » who wants to get started with data mining, then this book is for you. WHAT YOU WILL LEARN * Apply data mining concepts to real-world problems * Predict the outcome of sports matches based on past results * Determine the author of a document based on their writing style * Use APIs to download datasets from social media and other online services * Find and extract good features from difficult datasets * Create models that solve real-world problems * Design and develop data mining applications using a variety of datasets * Set up reproducible experiments and generate robust results * Recommend movies, online celebrities, and news articles based on personal preferences * Compute on big data, including real-time data from the Internet IN DETAIL The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations. « less
2015
250+ READY-TO-USE, POWERFUL DMX QUERIES Transform data mining model information into actionable business intelligence using the Data Mining Extensions (DMX) language. Practical DMX Queries for Microsoft SQL Server Analysis Services 2008 contains more than 250 downloadable DMX queries you can use to more » extract and visualize data. The application, syntax, and results of each query are described in detail. The book emphasizes DMX for use in SSMS against SSAS, but the queries also apply to SSRS, SSIS, DMX in SQL, WinForms, WebForms, and many other applications. Techniques for generating DMX syntax from graphical tools are also demonstrated in this valuable resource. * View cases within data mining structures and models using DMX Case queries * Examine the content of a data mining model with DMX Content queries * Perform DMX Prediction queries based on the Decision Trees algorithm and the Time Series algorithm * Run Prediction and Cluster queries based on the Clustering algorithm * Execute Prediction queries with Association and Sequence Clustering algorithms * Use DMX DDL queries to create, alter, drop, back up, and restore data mining objects * Display various parameters for each algorithm with Schema queries * Examine the values of discrete, discretized, and continuous structure columns using Column queries * Use graphical interfaces to generate Prediction, Content, Cluster, and DDL queries * Deliver DMX query results to end users Download the source code from www.mhprofessional.com/computingdownload « less
2010
A Beginner's Guide to Programming Images, Animation, and Interaction
The free, open-source Processing programming language environment was created at MIT for people who want to develop images, animation, and sound. Based on the ubiquitous Java, it provides an alternative to daunting languages and expensive proprietary software. This book gives graphic designers, artists more » and illustrators of all stripes a jump start to working with processing by providing detailed information on the basic principles of programming with the language, followed by careful, step-by-step explanations of select advanced techniques. The author teaches computer graphics at NYU's Tisch School of the Arts, and his book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. Previously announced as "Pixels, Patterns, and Processing" *A guided journey from the very basics of computer programming through to creating custom interactive 3D graphics *Step-by-step examples, approachable language, exercises, and LOTS of sample code support the reader's learning curve *Includes lessons on how to program live video, animated images and interactive sound « less
2008
Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence more » explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling. « less
2006
Advanced Approaches in Analyzing Unstructured Data
Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving more » approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities. « less
2006