Data Mining Concepts And Techniques
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Data Mining Concepts And Techniques
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Author : Jiawei Han
language : en
Publisher: Elsevier
Release Date : 2011-06-09
Data Mining Concepts And Techniques written by Jiawei Han and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-09 with Computers categories.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Data Mining
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Author : Jiawei Han
language : en
Publisher: Morgan Kaufmann
Release Date : 2022-07-02
Data Mining written by Jiawei Han and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-02 with Computers categories.
Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. - Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks - Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society - Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data - Visit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata
Data Mining
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Author : Micheline Kamber
language : en
Publisher:
Release Date : 2006
Data Mining written by Micheline Kamber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Data mining categories.
Data Mining Concepts And Techniques
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Author : Jiawei Han
language : en
Publisher:
Release Date : 2001
Data Mining Concepts And Techniques written by Jiawei Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Data mining categories.
教育部高等教育司推荐。
Data Mining
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Author : Jiawei Han
language : en
Publisher:
Release Date : 2001
Data Mining written by Jiawei Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Data mining categories.
Data Mining
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Author : Jiawei Han
language : en
Publisher:
Release Date : 2012
Data Mining written by Jiawei Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Algorithms categories.
Mining of Data with Complex Structures explores nature of data with complex structure including sequences, trees and graphs. Readers will find a detailed description of the state-of-the-art of sequence mining, tree mining and graph mining, and more.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2016-05-09
Data Mining For Business Analytics written by Galit Shmueli 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 2016-05-09 with Mathematics categories.
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.
Data Mining For Business Intelligence
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-28
Data Mining For Business Intelligence written by Galit Shmueli 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 2011-09-28 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 Mining Concepts And Techniques 2e
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Author : Jiawei Han
language : en
Publisher:
Release Date : 2010-01-01
Data Mining Concepts And Techniques 2e written by Jiawei Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-01 with Artificial intelligence categories.
Data Mining Concepts And Techniques
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Author : Mr. Harish Reddy Gantla
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2024-07-02
Data Mining Concepts And Techniques written by Mr. Harish Reddy Gantla and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-02 with Computers categories.
Data mining is the process of discovering patterns, correlations, and anomalies within large datasets to predict outcomes. Utilizing a variety of techniques drawn from statistics, machine learning, and database systems, data mining aims to transform raw data into useful information. Key concepts include classification, clustering, regression, association rule learning, and anomaly detection. These techniques enable businesses and researchers to make data-driven decisions, uncover hidden trends, and gain a competitive edge. Classification is a predictive modeling technique where a model is trained to categorize data into predefined classes. It's widely used in applications such as spam detection, medical diagnosis, and credit scoring. Clustering, on the other hand, is an unsupervised learning technique that groups data points into clusters based on their similarities. This approach is useful in market segmentation, image processing, and social network analysis. Regression analysis predicts a continuous outcome variable based on one or more predictor variables. It's essential in fields such as finance for forecasting stock prices or in marketing for predicting sales trends. Association rule learning identifies interesting relationships between variables in large databases, often used in market basket analysis to find products frequently bought together. Anomaly detection involves identifying rare items, events, or observations that differ significantly from the majority of the data. This is crucial in fraud detection, network security, and fault detection in industrial systems. The success of data mining projects relies on the quality of the data, the appropriateness of the mining algorithms used, and the proper interpretation of the results. The process of data mining typically involves several steps: data cleaning, data integration, data selection, data transformation, pattern discovery, and knowledge presentation. Each step is critical in ensuring that the final insights are accurate and actionable. As data mining continues to evolve, it is increasingly becoming integral to big data analytics, driving advancements in various domains such as healthcare, finance, marketing, and beyond.