Music Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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Music Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Want to Read Currently Reading Read. Refresh and try again. Open Preview See a Problem? Thanks for telling us about the problem. Return to Book Page. Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery.
DATA CLUSTERING Algorithms and Applications
Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool.
The book begins by exploring unsupervised, randomized, and causal feature selection.
It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k -means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection.
The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.
Learning with Case Studies, Second Edition. Learning with Case Studies, Second Edition strong 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, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R.
The book does not assume any prior knowledge about R. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine lear..
Text Mining and Visualization: Case Studies Using Open-Source Tools strong 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 software--explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards.
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Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. 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 understanding of the analytical techniques currently available to solve healthcare problems.
The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients.
Music Data Mining 1st Edition Tao Li, Mitsunori Ogihara, George Tzanetakis July 12, The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Francesco Bonchi, Elena Ferrari December 02, The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Please accept our apologies for any inconvenience this may cause. To ask other readers questions about Computational Methods of Feature Selection , please sign up.
Written by prominent re.. Mining Unstructured Information for Hypothesis Generation. 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 and important. Mining Unstructured Information for Hypothesis Generation strong describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses.
The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining,.. Practical Graph Mining with R. Discover Novel and Insightful Knowledge from Data Represented as a Graph br strong Practical Graph Mining with R strong presents a "do-it-yourself" approach to extracting interesting patterns from graph data.
It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Optimization Based Theory, Algorithms, and Extensions. Optimization Based Theory, Algorithms, and Extensions strong presents an accessible treatment of the two main components of support vector machines SVMs --classification problems and regression problems.
The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of temporal information in data today. Edited by Markus Hofmann , Ralf Klinkenberg. Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other….
Computational Methods of Feature Selection by Huan Liu
This class-tested textbook is designed for a semester-long graduate, or senior undergraduate course on Computational Health Informatics. Integrating a computer science perspective with a clinical perspective, the book is designed to prepare computer science students for careers in computational…. Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice.
Human Capital Systems, Analytics, and Data Mining provides human capital professionals, researchers, and students with a comprehensive and portable guide to human capital systems, analytics and data mining.
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The main purpose of this book is to provide a rich tool set of methods and tutorials for…. Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis EDA and introduces the range of "interesting" — good, bad, and ugly — features that can be found in data, and why it is important to find them.
It also introduces the mechanics of using R to…. Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified…. Edited by Guozhu Dong , Huan Liu.
- Chapman & Hall/CRC Data Mining and Knowledge Discovery Series.
- Chapman & Hall/CRC Data Mining and Knowledge Discovery Series.
- Music Data Mining.
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the….
Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and…. Edited by Ashok N. Srivastava , Ramakrishna Nemani , Karsten Steinhaeuser. By Quan Zheng , David Skillicorn.
Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and…. An emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year.
The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes…. 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.