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Author: C. Perez Publisher: Createspace Independent Publishing Platform ISBN: 9781979029735 Category : Languages : en Pages : 180
Book Description
The availability of large volumes of data and the use of computer tools has transformed the research and analysis of data orienting it towards certain specialized techniques included under the name of Data Mining. Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining and processing large amounts of data organized according to Big Data techniques. Data Mining methodologies include SAS Institute's SEMMA methodology and IBM's CRISP-DM methodology. -SAS Institute defines the concept of Data Mining as the process of Selecting, Exploring, Modifying, Modeling and Assessment large amounts of data with the aim of uncovering unknown knowledge in databases. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute. -IBM provides a complete methodology for ordering data mining tasks. The foundation is similar to SAS. CRISP-DM considers the process of extraction of knowledge from the data through 6 phases: Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Model deployment. MATLAB has tools to work in the different phases of Data Mining. In this book are developed several chapters that include phases of Data Mining. The chapter Data Processing includes Selection and Modification phases. The chapter Data Exploration includes the Exploring phase. Tthe chapters on Predictive Techniques include the Modlization phase. The chapter on Classification Techniques include the Modeling and Modification phases. All chapters are supplemented by examples that clarify the techniques.
Author: C. Perez Publisher: Createspace Independent Publishing Platform ISBN: 9781979029735 Category : Languages : en Pages : 180
Book Description
The availability of large volumes of data and the use of computer tools has transformed the research and analysis of data orienting it towards certain specialized techniques included under the name of Data Mining. Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining and processing large amounts of data organized according to Big Data techniques. Data Mining methodologies include SAS Institute's SEMMA methodology and IBM's CRISP-DM methodology. -SAS Institute defines the concept of Data Mining as the process of Selecting, Exploring, Modifying, Modeling and Assessment large amounts of data with the aim of uncovering unknown knowledge in databases. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute. -IBM provides a complete methodology for ordering data mining tasks. The foundation is similar to SAS. CRISP-DM considers the process of extraction of knowledge from the data through 6 phases: Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Model deployment. MATLAB has tools to work in the different phases of Data Mining. In this book are developed several chapters that include phases of Data Mining. The chapter Data Processing includes Selection and Modification phases. The chapter Data Exploration includes the Exploring phase. Tthe chapters on Predictive Techniques include the Modlization phase. The chapter on Classification Techniques include the Modeling and Modification phases. All chapters are supplemented by examples that clarify the techniques.
Author: Marvin L. Publisher: Createspace Independent Publishing Platform ISBN: 9781539712497 Category : Languages : en Pages : 0
Book Description
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. This book develops the more important Data Mining Tacniques.
Author: Rafael E. Banchs Publisher: Springer ISBN: 9781489994646 Category : Computers Languages : en Pages : 0
Book Description
Text Mining with MATLAB provides a comprehensive introduction to text mining using MATLAB. It’s designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The first part provides an introduction to basic procedures for handling and operating with text strings. Then, it reviews major mathematical modeling approaches. Statistical and geometrical models are also described along with main dimensionality reduction methods. Finally, it presents some specific applications such as document clustering, classification, search and terminology extraction. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.
Author: G. Peck Publisher: ISBN: 9781979444392 Category : Languages : en Pages : 426
Book Description
Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data. These patterns and trends can be collected and defined as a data mining model.MATLAB has tools to work in the different phases of Data Mining. In this book are developed several chapters that include phases of Data Mining. All chapters are supplemented by examples that clarify the techniques.
Author: Giuseppe Ciaburro Publisher: Packt Publishing Ltd ISBN: 1788399390 Category : Computers Languages : en Pages : 374
Book Description
Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Author: P. Braselton Publisher: Createspace Independent Publishing Platform ISBN: 9781979057769 Category : Languages : en Pages : 314
Book Description
Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data. These patterns and trends can be collected and defined as a data mining model. Mining models can be applied to specific scenarios, such as: - Forecasting: Estimating sales, predicting server loads or server downtime - Risk and probability: Choosing the best customers for targeted mailings, determining the probable break-even point for risk scenarios, assigning probabilities to diagnoses or other outcomes - Recommendations: Determining which products are likely to be sold together, generating recommendations - Finding sequences: Analyzing customer selections in a shopping cart, predicting next likely events - Grouping: Separating customers or events into cluster of related items, analyzing and predicting affinities Building a mining model is part of a larger process that includes everything from asking questions about the data and creating a model to answer those questions, to deploying the model into a working environment. The availability of large volumes of data and the use of computer tools has transformed the research and analysis of data orienting it towards certain specialized techniques included under the name of Data Mining. Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining and processing large amounts of data organized according to Big Data techniques. Data Mining methodologies include SAS Institute's SEMMA methodology and IBM's CRISP-DM methodology. - SAS Institute defines the concept of Data Mining as the process of Selecting, Exploring, Modifying, Modeling and Assessment large amounts of data with the aim of uncovering unknown knowledge in databases. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute. - IBM provides a complete methodology for ordering data mining tasks. The foundation is similar to SAS. CRISP-DM considers the process of extraction of knowledge from the data through 6 phases: Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Model deployment. MATLAB has tools to work in the different phases of Data Mining. In this book are developed several chapters that include phases of Data Mining. All chapters are supplemented by examples that clarify the techniques.
Author: Wendy L. Martinez Publisher: CRC Press ISBN: 1315349841 Category : Mathematics Languages : en Pages : 589
Book Description
Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." —Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data
Author: C Perez Publisher: Independently Published ISBN: 9781099848148 Category : Languages : en Pages : 388
Book Description
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions.The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.
Author: H. Mendel Publisher: ISBN: 9781979275859 Category : Languages : en Pages : 358
Book Description
The availability of large volumes of data and the use of computer tools has transformed the research and anlysis of data orienting it towards certain specialized techniques included under the name of Data Mining. Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining and processing large amounts of data organized according to Big Data techniques. Data Mining methodologies include SAS Institute's SEMMA methodology and IBM's CRISP-DM methodology. MATLAB has tools to work with the different techniques of Data Mining.On the other hand, Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. * Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. * Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. The techniques of data mining and machine learning may be considered to be closely related. Both concepts are very similar. Supervised machine learning techniques can be considered equivalent to the techniques of predictive modeling of data mining, and unsupervised machine learning techniques can be considered equivalent to classification techniques in data miningBig data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. A key tools in big data analytics are the neural networks tall arrays and paralell computing. MATLAB Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. This book develops several chapters that include advanced Data Mining techniques (Neural Networks, Segmentation and advanced Modelization techniques). All chapters are supplemented by examples that clarify the techniques. This book also develops supervised learning and unsupervised learning techniques across examples using MATLAB. As well, this book develops big data tecniques like tall arrays and paralell computing.
Author: C. Perez
Author: C. Perez
Author: Marvin L.
Author: Rafael E. Banchs
Author: G. Peck
Author: Giuseppe Ciaburro
Author: P. Braselton
Author: Arash Karimpour
Author: Wendy L. Martinez
Author: C Perez
Author: H. Mendel