4 edition of Data mining for scientific and engineering applications found in the catalog.
Data mining for scientific and engineering applications
Includes bibliographical references.
|Statement||edited by Robert L. Grossman ... [et al.].|
|Series||Massive computing, Massive computing (Unnumbered)|
|The Physical Object|
|Pagination||xx, 605 p. :|
|Number of Pages||605|
|ISBN 10||1402001142, 1402000332|
This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science. This research area covers mathematics and computational methods in data science that includes machine learning and data mining, intertwined with other key areas such as statistics, computer science, network science, and signal processing - all within the context of data science as well as applications of mathematical methods in data/information processing to science and .
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data . Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited “student-authored lectures” which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis.
Educational Data Mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from. Data Mining for Scientific and Engineering Applications. Due to advances in information technology and high performance computing are that are employed in the scientifc and engineering simulation, very large data sets are increasingly becoming available in many computational mechanics disciplines.
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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.
14 rows Data Mining Applications in Engineering and Medicine targets to help data miners Cited by: The Handbook of Statistical Analysis and Data Mining Applications. 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 one discern the technical and business problem, /5(42). Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes.
This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date.
The book is complete with theory and practical use cases. Description: This book is the proceedings of the Third International Conference on Computational Science and Engineering (ICCSE, August, Kota Kinabalu, Sabah, Malaysia) and contains the selected peer-reviewed papers which reflect recent achievements in the field of application of the computational methods and algorithms in scientific research and engineering.
Data mining and machine learning have endless applications in Civil Engineering and any other area really. The problem is how good is the data you. This class is a shorter, less in-depth version of Fundamentals of Data Mining, customized for the world of science. Obtain an overview of the methods, techniques, and processes of data mining, with an emphasis on scientific applications.
Explore a variety of scientific case studies learn how data mining can be applied to make meaningful. One solution is to use data mining. This book thus proposes an integration of techniques from data mining, a field of research where the aim is to find knowledge from data, into an existing multiple-model system identification methodology.
In addition to providing information about the candidate model space, data mining is found to be a Cited by: 3. The aim of this is to promote and research on Data Mining projects that allows us to produce more valuable information to people of different areas of interest. The membersof the group work in fields so varied as ontologies, computer science or engineering software.
Such fields are put together to obtain most of the data mining technology. Data mining for scientific and engineering applications /edited by Robert L.
Grossman [et al.] Dordrecht: Kluwer AcademicCited by: Book Description. A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast 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.
Text and data mining. Be more efficient: Web crawling is an inefficient method of harvesting large quantities of content and by using our APIs you can quickly and easily access and download the data you need.
Retrieve your data in a better format: Elsevier converts our journal articles and book chapters into XML, which is a format preferred by text miners. Data mining techniques hold the promise of assisting scientists and engineers in the analysis of massive, complex data sets, enabling them to make scientific discoveries, gain fundamental insights into the physical processes being studied, and advance their understanding of.
The book is triggered by pervasive applications that retrieve knowledge from real-world big data. Data mining finds applications in the entire spectrum of science and technology including basic sciences to life sciences and medicine, to social, economic, and cognitive sciences, to engineering and computers.
Becks, J.-C. Toebermann, in Computer Aided Chemical Engineering, 4 Conclusions. Data mining techniques are more and more frequently used on numerical or structured data to discover new knowledge and the benefit of such techniques is well proven.
However, knowledge captured in textual documentation is also a very valuable information source for any. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems.
This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems.
Book Abstract: Discover the benefits of applying algorithms to solve scientific, engineering, and practical problems Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in "hot" application areas, such as computational biology.
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. Collection of selected, peer reviewed papers from the 2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization (MEIEI ), September, Chongqing, China.
The papers are grouped as follows: Chapter 1: Applied Mechanics and Advances in Mechanical Engineering; Chapter 2: Industrial Electronics. Data Mining Theory, Methodology, Techniques, and Applications.
Visualisation and Exploration of Scientific Data Using Graphs. Pages Raymond, Ben (et al.) Preview. Book Title Data Mining Book Subtitle Theory, Methodology, Techniques, and.
Advances and challenges in building engineering and data mining applications for energy-efficient communities data mining (DM) is a powerful and versatile tool to automatically extract the valuable knowledge embedded in huge amounts of data.
This paper reviews the advances in the field of building engineering and DM applications over Cited by: Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a.
Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. Data Mining is all about discovering unsuspected/ previously unknown relationships amongst the data.
It is a multi-disciplinary skill that uses machine learning, statistics, AI and database technology. The insights derived via Data Mining can be used.