This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. ...Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
This open access book includes methods for retrieval, semantic representation, and analysis of Volunteered Geographic Information (VGI), geovisualization and user interactions related to VGI, and ...discusses selected topics in active participation, social context, and privacy awareness. It presents the results of the DFG-funded priority program "VGI: Interpretation, Visualization, and Social Computing" (2016-2023). The book includes three parts representing the principal research pillars within the program. Part I "Representation and Analysis of VGI" discusses recent approaches to enhance the representation and analysis of VGI. It includes semantic representation of VGI data in knowledge graphs; machine-learning approaches to VGI mining, completion, and enrichment as well as to the improvement of data quality and fitness for purpose. Part II "Geovisualization and User Interactions related to VGI" book explores geovisualizations and user interactions supporting the analysis and presentation of VGI data. When designing these visualizations and user interactions, the specific properties of VGI data, the knowledge and abilities of different target users, and technical viability of solutions need to be considered. Part III "Active Participation, Social Context and Privacy Awareness" of the book addresses the human impact associated with VGI. It includes chapters on the use of wearable sensors worn by volunteers to record their exposure to environmental stressors on their daily journeys, on the collective behavior of people using location-based social media and movement data from football matches, and on the motivation of volunteers who provide important support in information gathering, filtering and analysis of social media in disaster situations. The book is of interest to researchers and advanced professionals in geoinformation, cartography, visual analytics, data science and machine learning.
Web Mining Kumbhar, V.S; Oza, K. S; Kamat, R.K
2016, 2017-01-31
eBook
Web mining is the application of data mining strategies to excerpt learning from web information, i.e. web content, web structure, and web usage data. With the emergence of the web as the predominant ...and converging platform for communication, business and scholastic information dissemination, especially in the last five years, there are ever increasing research groups working on different aspects of web mining mainly in three directions: mining of web content, web structure, and web usage. In this context, there are a good number of frameworks and benchmarks related to the metrics of the websites which is certainly weighty for B2B, B2C and in general in any e-commerce paradigm. This book lays more emphasis on the classification and clustering aspects of the websites in order to come out with the true perception of the websites in light of their usability. In a nutshell, Web Mining: A Synergic Approach Resorting to Classifications and Clustering showcases an effective methodology for classification and clustering of web sites from their usability point of view. While the clustering and classification is accomplished by using an open source tool WEKA, the basic dataset for the selected websites has been emanated by using a free tool site-analyzer. As a case study, several commercial websites have been analyzed. The dataset preparation using site-analyzer and classification through WEKA by embedding different algorithms is one of the unique selling points of this book. This text projects a complete spectrum of web mining from its very inception through data mining and takes the reader up to the application level. Salient features of the book include: * Literature review of research work in the area of web mining * Business websites domain researched, and data collected using site-analyzer tool * Accessibility, design, text, multimedia, and networking are assessed * Datasets are filtered further by selecting vital attributes which are Search Engine Optimized for processing using the WEKA attributed tool * Dataset with labels have been classified using J48, RBFNetwork, NaïveBayes, and SMO techniques using Weka * A comparative analysis of all classifiers is reported * Commercial applications for improving website performance based on SEO is given
The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ...ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ...ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features:Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.Starts from basic principles up to advanced concepts.Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.Gives practical tips for data mining implementation to solve real world problems.Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means ...of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.
The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) have longstanding ethical practice standards that are explicitly intended to be utilized by all who use ...statistical practices or computing, or both. Since statistics and computing are critical in any data-centered activity, these practice standards are essential to instruction in the uses of statistical practices or computing across disciplines. Ethical Reasoning For A Data-Centered World is aimed at any undergraduate or graduate students utilizing data. Whether the career goal is research, teaching, business, government, or a combination, this book presents a method for understanding and prioritizing ethical statistics, computing, and data science - featuring the ASA and ACM practice standards. To facilitate engagement, integration with prior learning, and authenticity, the material is organized around seven tasks: Planning/Designing; Data collection; Analysis; Interpretation; Reporting; Documenting; and Engaging in team work. This book is a companion volume to Ethical Practice of Statistics and Data Science, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the American Statistical Association (ASA) and Association of Computing Machinery (ACM) ethical guideline documents.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time ...series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
