Principles And Theory For Data Mining And Machine Learning
DOWNLOAD eBooks
Download Principles And Theory For Data Mining And Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Principles And Theory For Data Mining And Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Principles And Theory For Data Mining And Machine Learning
DOWNLOAD eBooks
Author : Bertrand Clarke
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-21
Principles And Theory For Data Mining And Machine Learning written by Bertrand Clarke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-21 with Computers categories.
Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Principles Of Data Mining
DOWNLOAD eBooks
Author : David J. Hand
language : en
Publisher: MIT Press
Release Date : 2001-08-17
Principles Of Data Mining written by David J. Hand and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-17 with Computers categories.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Machine Learning And Data Mining
DOWNLOAD eBooks
Author : Igor Kononenko
language : en
Publisher: Elsevier
Release Date : 2007-04-30
Machine Learning And Data Mining written by Igor Kononenko and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-30 with Computers categories.
Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions. Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions
Principles Of Data Mining
DOWNLOAD eBooks
Author : Max Bramer
language : en
Publisher: Springer
Release Date : 2016-11-09
Principles Of Data Mining written by Max Bramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-09 with Computers categories.
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
Principles And Theories Of Data Mining With Rapidminer
DOWNLOAD eBooks
Author : Ramjan, Sarawut
language : en
Publisher: IGI Global
Release Date : 2023-05-09
Principles And Theories Of Data Mining With Rapidminer written by Ramjan, Sarawut and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Computers categories.
The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.
Principles And Theories Of Data Mining With Rapidminer
DOWNLOAD eBooks
Author : Sarawut Ramjan
language : en
Publisher:
Release Date : 2023
Principles And Theories Of Data Mining With Rapidminer written by Sarawut Ramjan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Big data categories.
"This book is academically written as a guide for students and people interested in experimenting Data Mining using RapidMiner software. It covers the contents related to Data Mining, which consists of Classification, Deep Learning, Association Rule, Clustering, Recommendation System and RapidMiner Software usage as well as researching case studies on the use of data mining techniques in data science. Additionally, this book is the foundation of Python programming for data science for young scientists who want to understand data mining algorithms. As well as starting to write programs that can be applied to other data science programs. At the end of this book, authors describe about data governance with a case study of the government sector to enable young data scientists to understand the role of data scientists as part of stakeholders in data governance actions. The authors hope that this book is a good beginning for those who would like to develop themselves or for those who own data within their organization to meet internal and external problems. RapidMiner software is used to analyze data and provide guidance for further study in data science at a higher level"--
Data Mining And Data Warehousing
DOWNLOAD eBooks
Author : Parteek Bhatia
language : en
Publisher: Cambridge University Press
Release Date : 2019-06-27
Data Mining And Data Warehousing written by Parteek Bhatia and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-27 with Computers categories.
Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.
Principles Of Data Mining And Knowledge Discovery
DOWNLOAD eBooks
Author : Jan Komorowski
language : en
Publisher: Springer Science & Business Media
Release Date : 1997-06-13
Principles Of Data Mining And Knowledge Discovery written by Jan Komorowski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-06-13 with Business & Economics categories.
This book constitutes the refereed proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '97, held in Trondheim, Norway, in June 1997. The volume presents a total of 38 revised full papers together with abstracts of one invited talk and four tutorials. Among the topics covered are data and knowledge representation, statistical and probabilistic methods, logic-based approaches, man-machine interaction aspects, AI contributions, high performance computing support, machine learning, automated scientific discovery, quality assessment, and applications.
Principles Of Data Mining And Knowledge Discovery
DOWNLOAD eBooks
Author : Luc de Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08-23
Principles Of Data Mining And Knowledge Discovery written by Luc de Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-23 with Computers categories.
This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001. The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.
Scientific Data Mining And Knowledge Discovery
DOWNLOAD eBooks
Author : Mohamed Medhat Gaber
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-09-19
Scientific Data Mining And Knowledge Discovery written by Mohamed Medhat Gaber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-19 with Computers categories.
Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.