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Author : Anonim
Publisher : Springer
Page : 628 pages
File Size : 52,5 Mb
Release : 2012-11-26
Category : Electronic
ISBN : 1461517346
Author : R.L. Grossman,C. Kamath,P. Kegelmeyer,V. Kumar,R. Namburu
Publisher : Springer Science & Business Media
Page : 608 pages
File Size : 44,6 Mb
Release : 2013-12-01
Category : Computers
ISBN : 9781461517337
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
Author : Anonim
Publisher : BoD – Books on Demand
Page : 226 pages
File Size : 49,9 Mb
Release : 2022-03-30
Category : Computers
ISBN : 9781839692666
The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.
Author : Bhatnagar, Vishal
Publisher : IGI Global
Page : 405 pages
File Size : 49,6 Mb
Release : 2014-05-31
Category : Computers
ISBN : 9781466660878
Particularly in the fields of software engineering, virtual reality, and computer science, data mining techniques play a critical role in the success of a variety of projects and endeavors. Understanding the available tools and emerging trends in this field is an important consideration for any organization. Data Mining and Analysis in the Engineering Field explores current research in data mining, including the important trends and patterns and their impact in fields such as software engineering. With a focus on modern techniques as well as past experiences, this vital reference work will be of greatest use to engineers, researchers, and practitioners in scientific-, engineering-, and business-related fields.
Author : S. Sumathi,S.N. Sivanandam
Publisher : Springer
Page : 828 pages
File Size : 51,7 Mb
Release : 2006-10-12
Category : Computers
ISBN : 9783540343516
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.
Author : Robert Nisbet,Gary Miner,Ken Yale
Publisher : Elsevier
Page : 822 pages
File Size : 44,7 Mb
Release : 2017-11-09
Category : Mathematics
ISBN : 9780124166455
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Author : Derya Birant
Publisher : BoD – Books on Demand
Page : 214 pages
File Size : 45,6 Mb
Release : 2021-01-20
Category : Computers
ISBN : 9781839683183
Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment.
Author : Chandrika Kamath
Publisher : SIAM
Page : 295 pages
File Size : 54,7 Mb
Release : 2009-01-01
Category : Mathematics
ISBN : 9780898717693
Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.
Author : Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 54,7 Mb
Release : 2022-01-26
Category : Computers
ISBN : 9781119792505
DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.
Author : Kukatlapalli Pradeep Kumar,Aynur Unal,Vinay Jha Pillai,Hari Murthy,M. Niranjanamurthy
Publisher : John Wiley & Sons
Page : 367 pages
File Size : 44,5 Mb
Release : 2023-08-29
Category : Mathematics
ISBN : 9781119841975
DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Author : Guozhu Dong,James Bailey
Publisher : CRC Press
Page : 428 pages
File Size : 53,6 Mb
Release : 2016-04-19
Category : Business & Economics
ISBN : 9781439854334
A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and
Author : Debabrata Samanta,SK Hafizul Islam,Naveen Chilamkurti,Mohammad Hammoudeh
Publisher : CRC Press
Page : 296 pages
File Size : 54,5 Mb
Release : 2022-04-05
Category : Technology & Engineering
ISBN : 9781000550429
With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.
Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1269 pages
File Size : 50,6 Mb
Release : 2010-09-10
Category : Computers
ISBN : 9780387098234
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Author : Rahman, Hakikur
Publisher : IGI Global
Page : 356 pages
File Size : 52,7 Mb
Release : 2008-07-31
Category : Technology & Engineering
ISBN : 9781599046594
Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.
Author : S. Sumathi,S.N. Sivanandam
Publisher : Springer Science & Business Media
Page : 836 pages
File Size : 42,6 Mb
Release : 2006-09-26
Category : Computers
ISBN : 9783540343509
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.