RapidMiner PDF Download

Author: Markus Hofmann
Publisher: CRC Press
ISBN: 1482205505
Category : Business & Economics
Languages : en
Pages : 518

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Book Description
Powerful, Flexible Tools for a Data-Driven WorldAs 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 incre

Author: Vijay Kotu
Publisher: Morgan Kaufmann
ISBN: 0128016507
Category : Computers
Languages : en
Pages : 447

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Book Description
Put Predictive Analytics into ActionLearn 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

Author: Markus Hofmann
Publisher: CRC Press
ISBN: 1498759866
Category : Business & Economics
Languages : en
Pages : 530

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Book Description
Powerful, Flexible Tools for a Data-Driven WorldAs 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 incre

Author: Ramjan, Sarawut
Publisher: IGI Global
ISBN: 1668447320
Category : Computers
Languages : en
Pages : 326

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Book Description
The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.

Author: Richard J. Roiger
Publisher: CRC Press
ISBN: 1498763987
Category : Business & Economics
Languages : en
Pages : 530

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Book Description
Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery Presents the most popular data mining algorithms in an easy to follow format Includes instructional tutorials on applying the various data mining algorithms Provides several interesting datasets ready to be mined Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike The latest version of all freely available materials can also be downloaded at: http://krypton.mnsu.edu/~sa7379bt/

Author: Nils Clausen
Publisher: BoD – Books on Demand
ISBN: 3839145767
Category : Technology & Engineering
Languages : en
Pages : 230

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Book Description
This book presents an in-depth introduction to the characteristics and capabilities of two data-mining and business intelligence software packages: RapidMiner and the Jaspersoft Business Intelligence (BI) Suite.

Author: Richard J. Roiger
Publisher: CRC Press
ISBN: 1498763995
Category : Business & Economics
Languages : en
Pages : 557

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Book Description
Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

Author: Sneha Kumari
Publisher: Emerald Group Publishing
ISBN: 1800438761
Category : Computers
Languages : en
Pages : 288

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Book Description
Data Science & Business Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.

Author: Matthew North
Publisher:
ISBN: 9781727102475
Category :
Languages : en
Pages : 310

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Book Description
Some say we live in the Information Age; others, the Social Age; and still others, the Big Data Age. Regardless of what name we give it, we live in an age that generates monumental amounts of data-in all different kinds of formats. In business, and in our personal lives, we use smartphones and tablets, web sites and watches; with apps and interfaces to shop, learn, entertain and inform. Businesses increasingly use technology to interact with consumers to provide marketing, customer service, product information and more. All of this technological activity generates data, and we're increasingly good at gathering, storing and analyzing it.Data mining can help to identify interesting patterns and messages that exist in data, often hidden beneath the surface. In this modern age of information systems, it is easier than ever before to extract meaning from data. From classification to prediction, data mining can help.In Data Mining for the Masses, Third Edition, professor Matt North-a former risk analyst and software engineer at eBay-uses simple examples and clear explanations with free, powerful software tools to teach you the basics of data mining. In this Third Edition, implementations of these examples are offered in current versions of the RapidMiner software, and in the increasingly popular R Statistical Package.You've got more data than ever before and you know it's got value, if only you can figure out how to get to it. This book can show you how. Let's start digging!

Author: Asimakopoulou, Eleana
Publisher: IGI Global
ISBN: 1799867382
Category : Computers
Languages : en
Pages : 255

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Book Description
Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.