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Data Analysis Machine Learning And Knowledge Discovery
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Book Synopsis Data Analysis, Machine Learning and Knowledge Discovery by : Myra Spiliopoulou
Download or read book Data Analysis, Machine Learning and Knowledge Discovery written by Myra Spiliopoulou and published by Springer Science & Business Media. This book was released on 2013-11-26 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.
Book Synopsis Feature Engineering for Machine Learning and Data Analytics by : Guozhu Dong
Download or read book Feature Engineering for Machine Learning and Data Analytics written by Guozhu Dong and published by CRC Press. This book was released on 2018-03-14 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Book Synopsis Machine Learning for Knowledge Discovery with R by : Kao-Tai Tsai
Download or read book Machine Learning for Knowledge Discovery with R written by Kao-Tai Tsai and published by CRC Press. This book was released on 2021-09-15 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations. Written by statistical data analysis practitioner for practitioners. The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.
Book Synopsis Data Mining and Machine Learning by : Mohammed J. Zaki
Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Michelangelo Ceci
Download or read book Machine Learning and Knowledge Discovery in Databases written by Michelangelo Ceci and published by Springer. This book was released on 2017-12-29 with total page 881 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Peggy Cellier
Download or read book Machine Learning and Knowledge Discovery in Databases written by Peggy Cellier and published by Springer Nature. This book was released on 2020-03-27 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.
Book Synopsis Machine Learning and Knowledge Discovery for Engineering Systems Health Management by : Ashok N. Srivastava
Download or read book Machine Learning and Knowledge Discovery for Engineering Systems Health Management written by Ashok N. Srivastava and published by CRC Press. This book was released on 2016-04-19 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.
Book Synopsis Advances in Machine Learning and Data Mining for Astronomy by : Michael J. Way
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and complexity of data in most scientific disciplines
Book Synopsis Data Science, Learning by Latent Structures, and Knowledge Discovery by : Berthold Lausen
Download or read book Data Science, Learning by Latent Structures, and Knowledge Discovery written by Berthold Lausen and published by Springer. This book was released on 2015-05-06 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.
Book Synopsis Advances in Knowledge Discovery and Data Mining by : Usama M. Fayyad
Download or read book Advances in Knowledge Discovery and Data Mining written by Usama M. Fayyad and published by . This book was released on 1996 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.
Book Synopsis Data Mining and Analysis by : Mohammed J. Zaki
Download or read book Data Mining and Analysis written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2014-05-12 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Book Synopsis Data Mining Methods for Knowledge Discovery by : Krzysztof J. Cios
Download or read book Data Mining Methods for Knowledge Discovery written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.
Book Synopsis The Essentials of Data Science: Knowledge Discovery Using R by : Graham J. Williams
Download or read book The Essentials of Data Science: Knowledge Discovery Using R written by Graham J. Williams and published by CRC Press. This book was released on 2017-07-28 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
Book Synopsis Interactive Knowledge Discovery and Data Mining in Biomedical Informatics by : Andreas Holzinger
Download or read book Interactive Knowledge Discovery and Data Mining in Biomedical Informatics written by Andreas Holzinger and published by Springer. This book was released on 2014-06-17 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Book Synopsis Knowledge Discovery from Data Streams by : Joao Gama
Download or read book Knowledge Discovery from Data Streams written by Joao Gama and published by CRC Press. This book was released on 2010-05-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents
Book Synopsis Data Mining and Knowledge Discovery with Evolutionary Algorithms by : Alex A. Freitas
Download or read book Data Mining and Knowledge Discovery with Evolutionary Algorithms written by Alex A. Freitas and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics
Book Synopsis Industrial Applications of Machine Learning by : Pedro Larrañaga
Download or read book Industrial Applications of Machine Learning written by Pedro Larrañaga and published by CRC Press. This book was released on 2018-12-12 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka
