Contents:
The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes….
Temporal data mining / Theophano Mitsa. p. cm. -- (Chapman & Hall/CRC data mining and knowledge discovery series). Includes bibliographical references. www.farmersmarketmusic.com: Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) (): Theophano Mitsa: Books.
Edited by Yukio Ohsawa , Katsutoshi Yada. Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems. The expert contributors discuss how data mining can identify valuable…. Edited by Jake Y. Chen , Stefano Lonardi. Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades.
Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research.
Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material,…. A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results.
The text guides students to understand how data mining can be employed to solve real problems and…. Edited by Michael J. Way , Jeffrey D. Scargle , Kamal M. Ali , Ashok N. Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy.
Due to the massive amount…. Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data.
Edited by Ioannis Pitas. Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of….
Edited by Markus Hofmann , Andrew Chisholm. Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: The contributors—all highly experienced with text mining and open-source…. Edited by Tao Li. The field, therefore, plays an important role in data-driven system management. Algorithms and Applications presents state-of-the-art event mining…. Unstructured Mining Approaches to Solve Complex Scientific Problems As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true….
Edited by Chandan K. Reddy , Charu C. At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear…. Edited by Charu C.
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification…. Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains.
Through many examples and challenging case studies from a variety…. These techniques and tools provide unprecedented insights into data, enabling better decision making…. Aggarwal , Chandan K. Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering,…. Edited by Nagiza F. The first part of the book discusses the key tools and techniques in considerable depth, with a focus on the applicable models and algorithms.
Building on this, the second part considers the application to bioinformatics, finance and business computing. An extensive bibliography is provided on each of the topics covered, which makes this book a valuable reference for both the novice and the established practitioner. The clear, concise and instructive style will make this book particularly attractive to graduate students, researchers and industry professionals. Her linkage of sophisticated concepts of temporal data mining to practical business issues, such as strategy, forecasting, financial scenario analysis, customer value and retention, operations and logistics management, etc.
Although the algorithms and computations are complex, a reader can learn that there are quantitative approaches to expose additional, possibly critical, insights about virtually any facet of a business. This book further illustrates the growing importance of business analytics and showcases the myriad opportunities available to savvy managers and entrepreneurs to use a system of tools to leverage the value of, and investment in, their data collection and mining efforts.
As someone who works on signal processing applications in the medical device industry, I found the topic of temporal data mining to be extremely relevant. Our work focuses primarily on time series analysis of evoked potentials. Analysis of these signals is complicated by interfering signals, which although variable, tend to fall into a fairly small number of stereotypical cases. The techniques described in chapter 2 for temporal data similarity calculations and in chapter 3 for temporal data classification have potential application in our work.
I found that Temporal Data Mining offered a valuable overview of these fields and gave interesting insight into topics related to gene discovery and bioinformatics. A major strength of the book is the large bibliography, which provides the reader with the tools to dig deeper into topics of interest. Would you like to tell us about a lower price?
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Straight Talk from the Frontline Paperback. The Signal and the Noise: Here's how restrictions apply. Review The book would be enlightening for a statistical reader wishing to learn about the development of more empirical, less formal, methods in parallel to the work being done by the statistical community.
Hand, International Statistical Review , 79 I very strongly recommend [this] monumental monograph for the classroom as a graduate text or as a standalone [book] for professionals such as engineers and scientists for their research. Giakos, IEEE Fellow, University of Akron, Ohio, USA Temporal Data Mining presents a comprehensive overview of the various mathematical and computational aspects of dynamical data processing, from database storage and retrieval to statistical modeling and inference. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population.
She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http: A survey of remarkable breadth, listing hundreds of sources: Evidently, this kind of volume does not let the author discuss any particular paper or topic in detail; at best, a reference is covered with a short paragraph.
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