الصفحة 1
الصفحة 1
Introduction to Data Mining and its Applications
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.
Interactive Curve Modeling : With Applications to Computer Graphics, Vision and Image Processing
Interactive curve modeling techniques and their applications are extremely useful in a number of academic and industrial settings, and specifically play a significant role in multidisciplinary problem solving, such as in font design, designing objects, CAD/CAM, medical operations, scientific data visualization, virtual reality, character recognition, and object recognition, etc. Various problems such as iris, fingerprint, and signature recognition, can also be intelligently solved and automated using curve techniques.
Interactive and dynamic graphics for data analysis : With R and Ggobi
This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models.
Intelligent Interactive multimedia systems for e-healthcare applications
It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more. It covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for health monitoring and management systems using machine learning techniques, and for fusion applications in medical image processing. The book goes into detail on the various methods and techniques for supporting multimedia systems for e-healthcare.
Implementing machine learning for finance : A systematic approach to predictive risk and performance analysis for investment portfolios
Introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. You will: Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management / Know the concepts of feature engineering, data visualization, and hyperparameter optimization / Design, build, and test supervised and unsupervised ML and DL models / Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices / Structure and optimize an investment portfolio with preeminent asset classes and measure the / underlying risk
Handbook of Data Visualization
This new volume in the series Springer Handbooks of Computational Statistics gives an overview of modern data visualization methods, both in theory and practice. There are definitive chapters on modern graphical tools such as mosaic plots, parallel coordinate plots and linked views. There are chapters dedicated to graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well as chapters on software for graphics.
Graphics of Large Datasets : Visualizing a Million
Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. There are considerable advantages in extending displays which are well-known and well-tried, both in understanding how best to make use of them in your work and in presenting results to others. It should also make the book readily accessible for readers who already have a little experience of drawing statistical graphics. All ideas are illustrated with displays from analyses of real datasets and the authors emphasize the importance of interpreting displays effectively. Graphics should be drawn to convey information and the book includes many insightful examples.
Formal concept analysis ; Vol. 3874 ; 4th International Conference, ICFCA 2006, Dresden, Germany, Feburary 13-17, 2006, Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Formal Concept Analysis, held in February 2006. The 17 revised full papers presented together with four invited papers were carefully reviewed and selected for inclusion in the book. The papers show advances in applied lattice and order theory and in particular scientific advances related to formal concept analysis and its practical applications: data and knowledge processing including data visualization, information retrieval, machine learning, data analysis and knowledge management.
Formal concept analysis ; Vol. 3403 ; 3rd International Conference, ICFCA 2005, Lens, France, February 14-18, 2005, Proceedings
This book constitutes a comprehensive and systematic presentation of the state of the art of formal concept analysis and its applications. The first part of the book is devoted to foundational and methodological topics. The contributions in the second part demonstrate how formal concept analysis is successfully used outside of mathematics, in linguistics, text retrieval, association rule mining, data analysis, and economics. The third part presents applications in software engineering.
Formal Concept Analysis ; 5th International Conference, ICFCA 2007, Clermont-Ferrand, France, February 12-16, 2007, Proceedings
This book constitutes the refereed proceedings of the 5th International Conference on Formal Concept Analysis, ICFCA 2007. The papers comprise state of the art research from foundational to applied lattice theory and related fields, all of which involve methods and techniques of formal concept analysis such as data visualization, information retrieval, machine learning, data analysis and knowledge management.
Exploratory Analysis of Spatial and Temporal Data : A Systematic Approach
Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing.
Diagrammatic Representation and Inference ; 11th International Conference, Diagrams 2020, Tallinn, Estonia, August 24–28, 2020, Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on the Theory and Application of Diagrams, Diagrams 2020, held in Tallinn, Estonia, in August 2020.* The 20 full papers and 16 short papers presented together with 18 posters were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: diagrams in mathematics; diagram design, principles, and classification; reasoning with diagrams; Euler and Venn diagrams; empirical studies and cognition; logic and diagrams; and posters.
Data visualization and analysis in second language research
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages.
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Machine Learning Algorithms Using Python Programming
Presents the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning.
Linked Open Data -- Creating Knowledge Out of Interlinked Data : Results of the LOD2 Project
Linked Open Data (LOD) is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. This book presents an overview on the results of the research project “LOD2 -- Creating Knowledge out of Interlinked Data”. LOD2 is a large-scale integrating project co-funded by the European Commission within the FP7 Information and Communication Technologies Work Program. Commencing in September 2010, this 4-year project comprised leading Linked Open Data research groups, companies, and service providers from across 11 European countries and South Korea.
Lattice : Multivariate Data Visualization with R
R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed.
Journal on Data Semantics X
Web semantics and semi-structured data , Semantic caching , Data warehousing and semantic data mining , Spatial, temporal, multimedia and multimodal semantics , Semantics in data visualization , Semantic services for mobile users , Supporting tools , Applications of semantic-driven approaches These topics are to be understood as specifically related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not from the topics in the list. While the physical appearance of the journal issues is like the books from the we- known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board.
Journal on Data Semantics VI
Data warehousing and semantic data mining • Spatial, temporal, multimedia and multimodal semantics • Semantics in data visualization • Semantic services for mobile users • Supporting tools • Applications of semantic-driven approaches These topics are to be understood as speci?cally related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearanceof the journal issues looks like the books from the well-known Springer LNCS series, the mode of operation is that of a jo- nal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of best papers from major conferences addressing data semantics issues. Special issues, foc- ing on a speci?c topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board.
Journal on Data Semantics IV
• Semantics in data visualization • Semantic services for mobile users • Supporting tools • Applications of semantic-driven approaches These topics are to be understood as specifically related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearance of the journal issues is like the books from the we- known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of the best papers from major conferences addressing data semantics issues. Special issues, focusing on a specific topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board. Finally, it is also possible that a journal issue be devoted to a single text.
