Exploring Data with RapidMiner

Exploring Data with RapidMiner

A step-by-step tutorial style using examples so that users of different levels will benefit from the facilities offered by RapidMiner.If you are a computer scientist or an engineer who has real data from which you want to extract value, ...

Author: Andrew Chisholm

Publisher: Packt Publishing Ltd

ISBN: 9781782169345

Category: Computers

Page: 162

View: 355

A step-by-step tutorial style using examples so that users of different levels will benefit from the facilities offered by RapidMiner.If you are a computer scientist or an engineer who has real data from which you want to extract value, this book is ideal for you. You will need to have at least a basic awareness of data mining techniques and some exposure to RapidMiner.
Categories: Computers

Text Mining and Visualization

Text Mining and Visualization

... Ireland, where he focuses on the areas of data mining, text mining, data
exploration and visualisation, and business ... practical use of RapidMiner for
unsupervised clustering and has authored a book titled Exploring Data with
RapidMiner.

Author: Markus Hofmann

Publisher: CRC Press

ISBN: 9781482237580

Category: Business & Economics

Page: 337

View: 944

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a w
Categories: Business & Economics

RapidMiner

RapidMiner

Functions Associated Algorithms Clustering K-means Association Association
rules - FPGrowth algorithm Clustering Clustering is a technique that is useful for
exploring data. It is particularly useful where there are many cases and no
obvious ...

Author: Markus Hofmann

Publisher: CRC Press

ISBN: 9781482205503

Category: Business & Economics

Page: 525

View: 345

Powerful, Flexible Tools for a Data-Driven World As the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining Process The book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.
Categories: Business & Economics

RapidMiner Second Edition

RapidMiner  Second Edition

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific ...

Author: Markus Hofmann

Publisher: CRC Press

ISBN: 1138197009

Category:

Page: 600

View: 461

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The new edition will update all chapters to RapidMiner 7, and will add at least six new chapters, including new chapters on text mining, time series, and educational data mining.
Categories:

Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

Concepts and Practice with RapidMiner Vijay Kotu, Bala Deshpande ... Data
exploration helps us to understand the data better, to prepare the data in a way
that makes advanced analysis possible, and sometimes to get the necessary
insights ...

Author: Vijay Kotu

Publisher: Morgan Kaufmann

ISBN: 9780128016503

Category: Computers

Page: 446

View: 565

Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You’ll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples
Categories: Computers

Open Source Data Warehousing and Business Intelligence

Open Source Data Warehousing and Business Intelligence

The Community Edition of RapidMiner (formerly “Yale”) is an open source toolkit
for data mining. ... KNIME allows integration of different data loading, processing,
transformation, analysis and visual exploration modules without the focus on ...

Author: Lakshman Bulusu

Publisher: CRC Press

ISBN: 9781466578760

Category: Computers

Page: 432

View: 238

Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW &
Categories: Computers

Learning Predictive Analytics with R

Learning Predictive Analytics with R

Get to grips with key data visualization and predictive analytic skills using R About This Book Acquire predictive analytic skills using various tools of R Make predictions about future events by discovering valuable information from data ...

Author: Eric Mayor

Publisher: Packt Publishing Ltd

ISBN: 9781782169369

Category: Computers

Page: 332

View: 276

Get to grips with key data visualization and predictive analytic skills using R About This Book Acquire predictive analytic skills using various tools of R Make predictions about future events by discovering valuable information from data using R Comprehensible guidelines that focus on predictive model design with real-world data Who This Book Is For If you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book. What You Will Learn Customize R by installing and loading new packages Explore the structure of data using clustering algorithms Turn unstructured text into ordered data, and acquire knowledge from the data Classify your observations using Naive Bayes, k-NN, and decision trees Reduce the dimensionality of your data using principal component analysis Discover association rules using Apriori Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression Use PMML to deploy the models generated in R In Detail R is statistical software that is used for data analysis. There are two main types of learning from data: unsupervised learning, where the structure of data is extracted automatically; and supervised learning, where a labeled part of the data is used to learn the relationship or scores in a target attribute. As important information is often hidden in a lot of data, R helps to extract that information with its many standard and cutting-edge statistical functions. This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naive Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages. Style and approach This is a practical book, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this book, but that can also be applied to any other data.
Categories: Computers

Applied Data Analytics Principles and Applications

Applied Data Analytics   Principles and Applications

This text provides some of the most sought after techniques in big data analytics.

Author: Johnson I. Agbinya

Publisher: River Publishers Signal, Image

ISBN: 8770220964

Category: Computers

Page: 300

View: 700

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very large data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualization systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
Categories: Computers

IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts.

Author: Keith McCormick

Publisher: Packt Pub Limited

ISBN: 1849685460

Category: Computers

Page: 382

View: 199

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.
Categories: Computers

R Predictive Analysis

R  Predictive Analysis

Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, ...

Author: Tony Fischetti

Publisher: Packt Publishing Ltd

ISBN: 9781788290852

Category: Computers

Page: 1065

View: 341

Master the art of predictive modeling About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Familiarize yourself with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, Naive Bayes, decision trees, text mining and so on. We emphasize important concepts, such as the bias-variance trade-off and over-fitting, which are pervasive in predictive modeling Who This Book Is For If you work with data and want to become an expert in predictive analysis and modeling, then this Learning Path will serve you well. It is intended for budding and seasoned practitioners of predictive modeling alike. You should have basic knowledge of the use of R, although it's not necessary to put this Learning Path to great use. What You Will Learn Get to know the basics of R's syntax and major data structures Write functions, load data, and install packages Use different data sources in R and know how to interface with databases, and request and load JSON and XML Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data Predict the future with reasonably simple algorithms Understand key data visualization and predictive analytic skills using R Understand the language of models and the predictive modeling process In Detail Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you'll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Data Analysis with R, Tony Fischetti Learning Predictive Analytics with R, Eric Mayor Mastering Predictive Analytics with R, Rui Miguel Forte Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of predictive modeling.
Categories: Computers

Predictive Analytics For Dummies

Predictive Analytics For Dummies

With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data.

Author: Dr. Anasse Bari

Publisher: John Wiley & Sons

ISBN: 9781119267003

Category: Business & Economics

Page: 456

View: 904

Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
Categories: Business & Economics

Modeling and Processing for Next Generation Big Data Technologies

Modeling and Processing for Next Generation Big Data Technologies

This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics.

Author: Fatos Xhafa

Publisher: Springer

ISBN: 9783319091778

Category: Computers

Page: 516

View: 310

This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is intended for researchers, scholars, advanced students, software developers and practitioners working at the forefront in their field.
Categories: Computers

Enablers of Organisational Learning Knowledge Management and Innovation

Enablers of Organisational Learning  Knowledge Management  and Innovation

This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author’s influence on text interpretation, and the validity checks used to justify ...

Author: Preethi Kesavan

Publisher: Springer

ISBN: 9811597928

Category: Business & Economics

Page: 308

View: 749

This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author’s influence on text interpretation, and the validity checks used to justify text interpretations. Vast quantities of text and qualitative data in organizations often go unexplored. Text analytics outlined in this book allow readers to understand the process of converting unstructured text data into meaningful data for analysis in order to measure employee opinions, feedback, and reviews through sentiment analysis to support fact-based decision making. The methods involve using NVivo and RapidMiner software to perform lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, memo creation, information extraction, association analysis, and visualization. The methodological approach in the book uses innovation theory as a sensitizing concept to lay the foundation for the analysis of research data, suggesting approaches for empirical exploration of organizational learning, knowledge management, and innovation practices amongst geographically dispersed individuals and team members. Based on data obtained from a private educational organization that has offices dispersed across Asia through focus group discussions and interviews on these topics, the author highlights the need for integrating organizational learning, knowledge management, and innovation to improve organizational performance, exploring perspectives on collective relationships and networks, organizational characteristics and structures, and tacit and overt values which influence such innovation initiatives. In the process, the author puts forward a new theory which is built on three themes: relationship and networks, knowledge sharing mechanisms, and the role of social cognitive schema that facilitate emergent learning, knowledge management, and innovation.
Categories: Business & Economics

Engineering and Mining Journal

Engineering and Mining Journal

... average of 46 39 oz . silver , 0:35 oz . gold and the previous annual report work
on the lodes and exploring has been 7.12 % lead per ton . ... tons , or only is ,
owing to deficiencies in the data given , an unsatisfactory docu- about 2 % less
than during the preceding year . ... things are in good condition for rapid miner ,
and only one year less than that of the men of the healthiest and economical
working .

Author:

Publisher:

ISBN: CHI:101894144

Category: Engineering

Page:

View: 151

Categories: Engineering

Graph Theoretic Approaches for Analyzing Large Scale Social Networks

Graph Theoretic Approaches for Analyzing Large Scale Social Networks

Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved ...

Author: Meghanathan, Natarajan

Publisher: IGI Global

ISBN: 9781522528159

Category: Computers

Page: 355

View: 667

Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.
Categories: Computers