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Author: Marvin L.
Publisher:
ISBN: 9781540754837
Category :
Languages : en
Pages : 180
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Book Description
A wide range of applications, such as R, SAS, MATLAB, and SPSS Statistics, provide a huge toolbox of methods to analyze large data and can be used by experts to find patterns and interesting structures in the data. Many of these tools are mainly programming languages, which assumes the analyst has deeper programming skills and an advanced background in IT and mathematics. Since this field is becoming more important, graphic user-interfaced data analysis software is starting to enter the market, providing "drag and drop" mechanisms for career changers and people who are not experts in programming or statistics.One of these easy to handle, data analytics applications is the IBM SPSS Modeler. This book is dedicated to the introduction and explanation of its data analysis power and focused in decision trees. The more important topics are the next: Decision Tree Models General Uses of Tree-Based Analysis C&RT Algorithms CHAID Algorithms QUEST Algorithms C5.0 Algorithms Decision Trees with IM SPSS Modeler Building a Decision Tree with the C5.0 Node Building a decision tree with the CHAID node The C&R Tree node and variable generation The QUEST node-Boosting & Imbalanced data Detection of diabetes-comparison of decision tree nodes Rule set and cross-validation with C5.0 The Auto Classifier Node Building a Stream with the Auto Classifier Node The Auto Classifier Model Nugget Models for credit rating with the Auto Classifier node SVM classifier Interactive decision Trees with IBM SPSS Modeler The Interactive Tree Builder Growing and Pruning the Tree Defining Custom Splits Customizing the Tree View Gains Risks The Growing Directives Generation Filter and Select Nodes Building a Tree Model Directly C&R Tree, CHAID, QUEST, and C 5.0 Models Nuggets Model Nuggets for Boosting, Bagging and Very Large Datasets
Author: Marvin L.
Publisher:
ISBN: 9781540754837
Category :
Languages : en
Pages : 180
Get Book Here
Book Description
A wide range of applications, such as R, SAS, MATLAB, and SPSS Statistics, provide a huge toolbox of methods to analyze large data and can be used by experts to find patterns and interesting structures in the data. Many of these tools are mainly programming languages, which assumes the analyst has deeper programming skills and an advanced background in IT and mathematics. Since this field is becoming more important, graphic user-interfaced data analysis software is starting to enter the market, providing "drag and drop" mechanisms for career changers and people who are not experts in programming or statistics.One of these easy to handle, data analytics applications is the IBM SPSS Modeler. This book is dedicated to the introduction and explanation of its data analysis power and focused in decision trees. The more important topics are the next: Decision Tree Models General Uses of Tree-Based Analysis C&RT Algorithms CHAID Algorithms QUEST Algorithms C5.0 Algorithms Decision Trees with IM SPSS Modeler Building a Decision Tree with the C5.0 Node Building a decision tree with the CHAID node The C&R Tree node and variable generation The QUEST node-Boosting & Imbalanced data Detection of diabetes-comparison of decision tree nodes Rule set and cross-validation with C5.0 The Auto Classifier Node Building a Stream with the Auto Classifier Node The Auto Classifier Model Nugget Models for credit rating with the Auto Classifier node SVM classifier Interactive decision Trees with IBM SPSS Modeler The Interactive Tree Builder Growing and Pruning the Tree Defining Custom Splits Customizing the Tree View Gains Risks The Growing Directives Generation Filter and Select Nodes Building a Tree Model Directly C&R Tree, CHAID, QUEST, and C 5.0 Models Nuggets Model Nuggets for Boosting, Bagging and Very Large Datasets
Author: L. Marvin Marvin (author)
Publisher:
ISBN: 9781716130380
Category :
Languages : en
Pages : 0
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Book Description
Author: Keith McCormick
Publisher: Packt Publishing Ltd
ISBN: 1788296826
Category : Computers
Languages : en
Pages : 231
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Book Description
Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up–and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface Import data into Modeler and learn how to properly declare metadata Obtain summary statistics and audit the quality of your data Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields Assess simple relationships using various statistical and graphing techniques Get an overview of the different types of models available in Modeler Build a decision tree model and assess its results Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model's performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models. Style and approach This book empowers users to build practical & accurate predictive models quickly and intuitively. With the support of the advanced analytics users can discover hidden patterns and trends.This will help users to understand the factors that influence them, enabling you to take advantage of business opportunities and mitigate risks.
Author: César Pérez
Publisher: Createspace Independent Pub
ISBN: 9781490440699
Category : Computers
Languages : en
Pages : 242
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Book Description
This book presents the most common techniques used in data mining in a simple and easy to understand through one of the most common software solutions from among those existing in the market, in particular, IBM SPSS CLEMENTINE whose current name is IBM SPSS MODELER. Pursued as initial aim clarifying the applications concerning methods traditionally rated as difficult or dull. It seeks to present applications in data mining without having to manage high mathematical developments or complicated theoretical algorithms, which is the most common reason for the difficulties in understanding and implementation of this matter. Today data mining is used in different fields of science. Noteworthy applications in banking, and financial analysis of markets and trade, insurance and private health, in education, in industrial processes, in medicine, biology and bioengineering, telecommunications and in many other areas. Essentials to get started in data mining, regardless of the field in which it is applied, is the understanding of own concepts, task that does not require nor much less the domain of scientific apparatus involved in the matter. Later, when either necessary operative advanced, computer programs allow the results without having to decipher the mathematical development of the algorithms that are under the procedures. This book describes the simplest possible data mining concepts, so that they are understandable by readers with different training. The chapters begin describing the techniques in affordable language and then presenting the way to treat them through practical applications. An important part of each chapter are case studies completely resolved, including the interpretation of the results, which is precisely the most important thing in any matter with which they work. The book begins with an introduction to mining data and its phases. In successive chapters develop the initial phases (selection of information, data exploration, data cleansing, transformation of data, etc.). Subsequently elaborates on specific data mining, both predictive and descriptive techniques. Predictive techniques covers all models of regression, discriminant analysis, decision trees, neural networks and other techniques based on models. The descriptive techniques vary dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques.
Author: Lior Rokach
Publisher: World Scientific
ISBN: 9812771727
Category : Computers
Languages : en
Pages : 263
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Book Description
This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:: Self-explanatory and easy to follow when compacted; Able to handle a variety of input data: nominal, numeric and textual; Able to process datasets that may have errors or missing values; High predictive performance for a relatively small computational effort; Available in many data mining packages over a variety of platforms; Useful for various tasks, such as classification, regression, clustering and feature selection . Sample Chapter(s). Chapter 1: Introduction to Decision Trees (245 KB). Chapter 6: Advanced Decision Trees (409 KB). Chapter 10: Fuzzy Decision Trees (220 KB). Contents: Introduction to Decision Trees; Growing Decision Trees; Evaluation of Classification Trees; Splitting Criteria; Pruning Trees; Advanced Decision Trees; Decision Forests; Incremental Learning of Decision Trees; Feature Selection; Fuzzy Decision Trees; Hybridization of Decision Trees with Other Techniques; Sequence Classification Using Decision Trees. Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.
Author: Ken Yale
Publisher: Elsevier
ISBN: 0124166458
Category : Mathematics
Languages : en
Pages : 824
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Book Description
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Author: Tilo Wendler
Publisher: Springer Nature
ISBN: 3030543382
Category : Computers
Languages : en
Pages : 1285
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Book Description
Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.
Author: Osvaldo Gervasi
Publisher: Springer
ISBN: 3319421115
Category : Computers
Languages : en
Pages : 601
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Book Description
The five-volume set LNCS 9786-9790 constitutes the refereed proceedings of the 16th International Conference on Computational Science and Its Applications, ICCSA 2016, held in Beijing, China, in July 2016. The 239 revised full papers and 14 short papers presented at 33 workshops were carefully reviewed and selected from 849 submissions. They are organized in five thematical tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and technologies.
Author: Tilo Wendler
Publisher: Springer
ISBN: 9783030543372
Category : Computers
Languages : en
Pages : 0
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Book Description
Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.
Author: Whei-Jen Chen
Publisher: IBM Redbooks
ISBN: 073844118X
Category : Computers
Languages : en
Pages : 266
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Book Description
Systems of record (SORs) are engines that generates value for your business. Systems of engagement (SOE) are always evolving and generating new customer-centric experiences and new opportunities to capitalize on the value in the systems of record. The highest value is gained when systems of record and systems of engagement are brought together to deliver insight. Systems of insight (SOI) monitor and analyze what is going on with various behaviors in the systems of engagement and information being stored or transacted in the systems of record. SOIs seek new opportunities, risks, and operational behavior that needs to be reported or have action taken to optimize business outcomes. Systems of insight are at the core of the Digital Experience, which tries to derive insights from the enormous amount of data generated by automated processes and customer interactions. Systems of Insight can also provide the ability to apply analytics and rules to real-time data as it flows within, throughout, and beyond the enterprise (applications, databases, mobile, social, Internet of Things) to gain the wanted insight. Deriving this insight is a key step toward being able to make the best decisions and take the most appropriate actions. Examples of such actions are to improve the number of satisfied clients, identify clients at risk of leaving and incentivize them to stay loyal, identify patterns of risk or fraudulent behavior and take action to minimize it as early as possible, and detect patterns of behavior in operational systems and transportation that lead to failures, delays, and maintenance and take early action to minimize risks and costs. IBM® Operational Decision Manager is a decision management platform that provides capabilities that support both event-driven insight patterns, and business-rule-driven scenarios. It also can easily be used in combination with other IBM Analytics solutions, as the detailed examples will show. IBM Operational Decision Manager Advanced, along with complementary IBM software offerings that also provide capability for systems of insight, provides a way to deliver the greatest value to your customers and your business. IBM Operational Decision Manager Advanced brings together data from different sources to recognize meaningful trends and patterns. It empowers business users to define, manage, and automate repeatable operational decisions. As a result, organizations can create and shape customer-centric business moments. This IBM Redbooks® publication explains the key concepts of systems of insight and how to implement a system of insight solution with examples. It is intended for IT architects and professionals who are responsible for implementing a systems of insights solution requiring event-based context pattern detection and deterministic decision services to enhance other analytics solution components with IBM Operational Decision Manager Advanced.
