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Advanced Data Mining Techniques Classification Clustering Regression And Prediction
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Book Synopsis Advanced Data Mining Techniques: Classification, Clustering, Regression and Prediction by : Mr.Chitra Sabapathy Ranganathan
Download or read book Advanced Data Mining Techniques: Classification, Clustering, Regression and Prediction written by Mr.Chitra Sabapathy Ranganathan and published by Leilani Katie Publication. This book was released on 2024-04-02 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA
Book Synopsis Advanced Data Mining, Machine Learning and Big Data With Matlab by : H. Mendel
Download or read book Advanced Data Mining, Machine Learning and Big Data With Matlab written by H. Mendel and published by . This book was released on 2017-10-30 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Mining for Business Analytics by : Galit Shmueli
Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2016-06-13 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Data Mining Techniques by : David L. Olson
Download or read book Advanced Data Mining Techniques written by David L. Olson and published by Springer Science & Business Media. This book was released on 2008-01-01 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Data Mining and Applications by : Shuigeng Zhou
Download or read book Advanced Data Mining and Applications written by Shuigeng Zhou and published by Springer Science & Business Media. This book was released on 2012-12-09 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Data Mining and Applications by : Hiroshi Motoda
Download or read book Advanced Data Mining and Applications written by Hiroshi Motoda and published by Springer. This book was released on 2013-12-16 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Classification and Incremental Clustering in Data Mining and Machine Learning by : Sanjay Chakraborty
Download or read book Data Classification and Incremental Clustering in Data Mining and Machine Learning written by Sanjay Chakraborty and published by Springer Nature. This book was released on 2022-05-10 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cluster Analysis for Data Mining and System Identification by : János Abonyi
Download or read book Cluster Analysis for Data Mining and System Identification written by János Abonyi and published by Springer Science & Business Media. This book was released on 2007-06-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Community Quality-of-Life Indicators by : M. Joseph Sirgy
Download or read book Community Quality-of-Life Indicators written by M. Joseph Sirgy and published by Springer Nature. This book was released on 2022-08-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Classification, Clustering, and Data Mining Applications by : David Banks
Download or read book Classification, Clustering, and Data Mining Applications written by David Banks and published by Springer Science & Business Media. This book was released on 2011-01-07 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence by : Trivedi, Shrawan Kumar
Download or read book Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence written by Trivedi, Shrawan Kumar and published by IGI Global. This book was released on 2017-02-14 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.
Book Synopsis Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches by : Ali, A B M Shawkat
Download or read book Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches written by Ali, A B M Shawkat and published by IGI Global. This book was released on 2009-11-30 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book discusses advances in modern data mining research in today's rapidly growing global and technological environment"--Provided by publisher.
Book Synopsis Advanced Data Mining and Applications by : Longbing Cao
Download or read book Advanced Data Mining and Applications written by Longbing Cao and published by Springer. This book was released on 2010-11-18 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
Book Synopsis R: Mining spatial, text, web, and social media data by : Bater Makhabel
Download or read book R: Mining spatial, text, web, and social media data written by Bater Makhabel and published by Packt Publishing Ltd. This book was released on 2017-06-19 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.
Book Synopsis Advanced Methods for Knowledge Discovery from Complex Data by : Ujjwal Maulik
Download or read book Advanced Methods for Knowledge Discovery from Complex Data written by Ujjwal Maulik and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.
Book Synopsis Data Mining: Introductory And Advanced Topics by : Margaret H Dunham
Download or read book Data Mining: Introductory And Advanced Topics written by Margaret H Dunham and published by Pearson Education India. This book was released on 2006-09 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Durga Prasad Mohapatra Publisher :Springer Science & Business Media ISBN 13 :8132216652 Total Pages :1314 pages Book Rating :4.1/5 (322 download)
Book Synopsis Intelligent Computing, Networking, and Informatics by : Durga Prasad Mohapatra
Download or read book Intelligent Computing, Networking, and Informatics written by Durga Prasad Mohapatra and published by Springer Science & Business Media. This book was released on 2013-12-17 with total page 1314 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is composed of the Proceedings of the International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2013), held at Central Institute of Technology, Raipur, Chhattisgarh, India during June 14–16, 2013. The book records current research articles in the domain of computing, networking, and informatics. The book presents original research articles, case-studies, as well as review articles in the said field of study with emphasis on their implementation and practical application. Researchers, academicians, practitioners, and industry policy makers around the globe have contributed towards formation of this book with their valuable research submissions.
