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.
Data Mining And Exploration
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Author : Chong Ho Yu
language : en
Publisher:
Release Date : 2022
Data Mining And Exploration written by Chong Ho Yu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Data mining categories.
"This book will introduce 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. 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 these two schools of thought, and 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.). To remediate this problem, this book will provide the readers with the details of the similarities and differences between classical methods and data science, as well as the path for the transition (e.g. from p value to LogWorth, from resampling to ensemble methods, from content analysis to text mining...etc.)"--
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.
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
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.
Visual Data Mining
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Author : Simeon Simoff
language : en
Publisher: Springer
Release Date : 2008-07-23
Visual Data Mining written by Simeon Simoff and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-23 with Computers categories.
Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .
Data Preparation For Data Mining
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Author : Dorian Pyle
language : en
Publisher: Morgan Kaufmann
Release Date : 1999-03-22
Data Preparation For Data Mining written by Dorian Pyle and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-22 with Computers categories.
This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.
Predictive Analytics And Data Mining
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Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-11-27
Predictive Analytics And Data Mining written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-27 with Computers categories.
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
Information Visualization In Data Mining And Knowledge Discovery
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Author : Usama M. Fayyad
language : en
Publisher: Morgan Kaufmann
Release Date : 2002
Information Visualization In Data Mining And Knowledge Discovery written by Usama M. Fayyad and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration
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Author : Earl Cox
language : en
Publisher: Elsevier
Release Date : 2005-02-24
Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration written by Earl Cox and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-24 with Computers categories.
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems Helps you to understand the trade-offs implicit in various models and model architectures Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem Presents examples in C, C++, Java, and easy-to-understand pseudo-code Extensive online component, including sample code and a complete data mining workbench