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Author: Mr.Chitra Sabapathy Ranganathan
Publisher: Leilani Katie Publication
ISBN: 819721381X
Category : Computers
Languages : en
Pages : 155
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Book Description
Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA
Author: Mr.Chitra Sabapathy Ranganathan
Publisher: Leilani Katie Publication
ISBN: 819721381X
Category : Computers
Languages : en
Pages : 155
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Book Description
Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA
Author: H. Mendel
Publisher:
ISBN: 9781979275859
Category :
Languages : en
Pages : 358
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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: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 1118877438
Category : Mathematics
Languages : en
Pages : 464
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Book Description
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.
Author: David L. Olson
Publisher: Springer Science & Business Media
ISBN: 354076917X
Category : Business & Economics
Languages : en
Pages : 182
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Book Description
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.
Author: Shuigeng Zhou
Publisher: Springer Science & Business Media
ISBN: 3642355277
Category : Computers
Languages : en
Pages : 812
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Book Description
This book constitutes the refereed proceedings of the 8th International Conference on Advanced Data Mining and Applications, ADMA 2012, held in Nanjing, China, in December 2012. The 32 regular papers and 32 short papers presented in this volume were carefully reviewed and selected from 168 submissions. They are organized in topical sections named: social media mining; clustering; machine learning: algorithms and applications; classification; prediction, regression and recognition; optimization and approximation; mining time series and streaming data; Web mining and semantic analysis; data mining applications; search and retrieval; information recommendation and hiding; outlier detection; topic modeling; and data cube computing.
Author: Hiroshi Motoda
Publisher: Springer
ISBN: 3642539173
Category : Computers
Languages : en
Pages : 556
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Book Description
The two-volume set LNAI 8346 and 8347 constitutes the thoroughly refereed proceedings of the 9th International Conference on Advanced Data Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013. The 32 regular papers and 64 short papers presented in these two volumes were carefully reviewed and selected from 222 submissions. The papers included in these two volumes cover the following topics: opinion mining, behavior mining, data stream mining, sequential data mining, web mining, image mining, text mining, social network mining, classification, clustering, association rule mining, pattern mining, regression, predication, feature extraction, identification, privacy preservation, applications, and machine learning.
Author: Sanjay Chakraborty
Publisher: Springer Nature
ISBN: 3030930882
Category : Technology & Engineering
Languages : en
Pages : 210
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Book Description
This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
Author: David Banks
Publisher: Springer Science & Business Media
ISBN: 3642171036
Category : Language Arts & Disciplines
Languages : en
Pages : 642
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Book Description
This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Author: János Abonyi
Publisher: Springer Science & Business Media
ISBN: 3764379871
Category : Mathematics
Languages : en
Pages : 317
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Book Description
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.
Author: M. Joseph Sirgy
Publisher: Springer Nature
ISBN: 3031102088
Category : Social Science
Languages : en
Pages : 220
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Book Description
This training book is designed to help professionals enhance their knowledge of community quality-of-life indicators, and to develop viable community projects. Chapter 1 describes the theoretical concepts that guide the formulation of community indicator projects. Chapter 2 creates a sample community indicator project as a template of the entire process. Chapter 3 describes the planning process: how to identify sponsors, secure funding, develop an organizational structure, select a quality-of-life model, select indicators, and so on. Chapter 4 focuses on data collection. Finally, Chapter 5 describes efforts related to dissemination and promotion of community indicators projects. Written by a stalwart in the field of quality-of-life research, this book provides the tools of sound community project planning for quality-of-life researchers, social workers, social marketers, community research organizations, and policy-makers.
