Data Mining And Exploration
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Data Mining And Exploration
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Author : Chong Ho Alex Yu
language : en
Publisher: CRC Press
Release Date : 2022-10-27
Data Mining And Exploration written by Chong Ho Alex Yu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-27 with Business & Economics categories.
This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
About The Exploration Of Data Mining Techniques Using Structured Features For Information Extraction
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Author : Felix Jungermann
language : en
Publisher:
Release Date : 2012
About The Exploration Of Data Mining Techniques Using Structured Features For Information Extraction written by Felix Jungermann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Improving The Data Mining Exploration Technique For Job Shop Schedules By Using Multiple Data Sets
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Author : Shishir A. Kantak
language : en
Publisher:
Release Date : 2003
Improving The Data Mining Exploration Technique For Job Shop Schedules By Using Multiple Data Sets written by Shishir A. Kantak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Data mining categories.
Data Mining For Business Intelligence
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Author : Galit Shmueli
language : en
Publisher: John Wiley and Sons
Release Date : 2011-06-10
Data Mining For Business Intelligence written by Galit Shmueli and has been published by John Wiley and Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-10 with Mathematics categories.
Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." —Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." —computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Data Exploration Using Example Based Methods
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Author : Matteo Lissandrini
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-11-27
Data Exploration Using Example Based Methods written by Matteo Lissandrini and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Effects Of Data Exploration And Use Of Data Mining Tools To Extract Knowledge From Databases Kdd In Early Stages Of The Engineering Design Process Edp
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Author : Ma Lorena Escandon-Quintanilla
language : en
Publisher:
Release Date : 2017
Effects Of Data Exploration And Use Of Data Mining Tools To Extract Knowledge From Databases Kdd In Early Stages Of The Engineering Design Process Edp written by Ma Lorena Escandon-Quintanilla and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Exploration Warehousing
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Author : W. H. Inmon
language : en
Publisher: Wiley
Release Date : 2000-06-19
Exploration Warehousing written by W. H. Inmon and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-19 with Computers categories.
A revolutionary new approach to unearthing business opportunities, from the father of the data warehouseLet Bill Inmon, the father of the data warehouse, along with Robert Terdeman and Claudia Imhoff, introduce you to exploration warehousing, an innovative new approach to finding business opportunities hidden in patterns of data. In this groundbreaking book, they clearly explain the exploration process and identify the types of data warehouse designs best suited for exploration. They then outline the steps that must be followed in order to turn data into a competitive advantage. Using numerous case examples, the authors describe original exploration techniques and demonstrate how IT managers can work together with business managers to identify significant value in the data. These patterns can reveal opportunities in the marketplace for new products and services, when to discontinue products and services, where to streamline operations, and much more. To verify the strength and accuracy of these patterns, they show you how to use exploration with data mining techniques to assure business value. With this book, you'll gain a better understanding of: - The process of exploring data - The infrastructure of exploration - The roles that analysts play in your organization - How to form a basis of data that can be used for analysis - How patterns in data can be turned into business opportunity - When patterns should not be turned into business opportunity - The role of data mining
Data Preparation Tool For Exploration In Data Mining
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Author : Hock Heng Lee
language : en
Publisher:
Release Date : 2007
Data Preparation Tool For Exploration In Data Mining written by Hock Heng Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Data mining categories.
Visual Data Mining
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Author : Simeon Simoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-07-18
Visual Data Mining written by Simeon Simoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-18 with Computers categories.
The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.
Exploratory Data Mining And Data Cleaning
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Author : Tamraparni Dasu
language : en
Publisher: John Wiley & Sons
Release Date : 2003-08-01
Exploratory Data Mining And Data Cleaning written by Tamraparni Dasu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Mathematics categories.
Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.