Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements.

The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data.

This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book "Variants of Evolutionary Algorithms for Real-World Applications" aims to promote the practitioner's view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem's special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization.

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics?including classification, clustering, statistical learning, association analysis, and link mining?in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases

Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field.

New to the Second Edition

• Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques
• New chapter on data quality issues in GKD
• New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases
• New chapter that discusses the use of geographically weighted regression as an exploratory technique
• New chapter that gives an integrated approach to multivariate analysis and geovisualization
• Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases

Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.

This book constitutes the refereed proceedings of the Third International Conference on Intelligence Science, ICIS 2018, held in Beijing China, in November 2018. The 44 full papers and 5 short papers presented were carefully reviewed and selected from 85 submissions. They deal with key issues in intelligence science and have been organized in the following topical sections: brain cognition; machine learning; data intelligence; language cognition; perceptual intelligence; intelligent robots; fault diagnosis; and ethics of artificial intelligence.

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field

Giving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search.

The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use.

There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.

Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors' recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as "NP-hard" and the "phase transitions" occurring on the search landscape. Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors' recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling. The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.

Recentyearshaveseentheadventanddevelopmentofmanydevicesabletorecordand storeaneverincreasingamountofinformation. Thefastprogressofthesetechnologies is ubiquitousthroughoutall ?elds of science and applied contexts, ranging from medicine,biologyandlifesciences,toeconomicsandindustry. Thedataprovided bytheseinstrumentshavedifferentforms:2D-3Dimagesgeneratedbydiagnostic medicalscanners,computervisionorsatelliteremotesensing,microarraydataand genesets,integratedclinicalandadministrativedatafrompublichealthdatabases, realtimemonitoringdataofabio-marker,systemcontroldatasets. Allthesedata sharethecommoncharacteristicofbeingcomplexandoftenhighlydimensional. Theanalysisofcomplexandhighlydimensionaldataposesnewchallengesto thestatisticianandrequiresthedevelopmentofnovelmodelsandtechniques,fueling manyfascinatingandfastgrowingresearchareasofmodernstatistics. Anincomplete listincludes for example: functionaldata analysis, that deals with data having a functionalnature,suchascurvesandsurfaces;shapeanalysisofgeometricforms,that relatestoshapematchingandshaperecognition,appliedtocomputationalvisionand medicalimaging;datamining,thatstudiesalgorithmsfortheautomaticextraction ofinformationfromdata,elicitingrulesandpatternsoutofmassivedatasets;risk analysis,fortheevaluationofhealth,environmental,andengineeringrisks;graphical models,thatallowproblemsinvolvinglarge-scalemodelswithmillionsofrandom variableslinkedincomplexwaystobeapproached;reliabilityofcomplexsystems, whoseevaluationrequirestheuseofmanystatisticalandprobabilistictools;optimal designofcomputersimulationstoreplaceexpensiveandtimeconsumingphysical experiments. Thecontributionspublishedinthisvolumearetheresultofaselectionbasedonthe presentations(aboutonehundred)givenattheconference"S. Co. 2009:Complexdata modelingandcomputationallyintensivemethodsforestimationandprediction",held ? atthePolitecnicodiMilano. S. Co. isaforumforthediscussionofnewdevelopments ? September14-16,2009. Thatof2009isitssixthedition,the?rstonebeingheldinVenice in1999. VI Preface andapplicationsofstatisticalmethodsandcomputationaltechniquesforcomplexand highlydimensionaldatasets. Thebookisaddressedtostatisticiansworkingattheforefrontofthestatistical analysisofcomplexandhighlydimensionaldataandoffersawidevarietyofstatistical models,computerintensivemethodsandapplications. Wewishtothankallassociateeditorsandrefereesfortheirvaluablecontributions thatmadethisvolumepossible. MilanandVenice,May2010 PietroMantovan PiercesareSecchi Contents Space-timetextureanalysisinthermalinfraredimagingforclassi?cation ofRaynaud'sPhenomenon GrazianoAretusi,LaraFontanella,LuigiIppolitiandArcangeloMerla...1 Mixed-effectsmodellingofKevlar?brefailuretimesthroughBayesian non-parametrics RaffaeleArgiento,AlessandraGuglielmiandAntonioPievatolo...13 Space?llingandlocallyoptimaldesignsforGaussianUniversalKriging AlessandroBaldiAntogniniandMaroussaZagoraiou...27 Exploitation,integrationandstatisticalanalysisofthePublicHealth DatabaseandSTEMIArchiveintheLombardiaregion PietroBarbieri,Niccolo'Grieco,FrancescaIeva,AnnaMariaPaganoniand PiercesareSecchi...41 Bootstrapalgorithmsforvarianceestimationin PSsampling AlessandroBarbieroandFulviaMecatti...5 7 FastBayesianfunctionaldataanalysisofbasalbodytemperature JamesM. Ciera...71 AparametricMarkovchaintomodelage-andstate-dependentwear processes MassimilianoGiorgio,MaurizioGuidaandGianpaoloPulcini...85 CasestudiesinBayesiancomputationusingINLA SaraMartinoandHav ? ardRue...99 Agraphicalmodelsapproachforcomparinggenesets M. So?aMassa,MonicaChiognaandChiaraRomualdi...115 VIII Contents Predictivedensitiesandpredictionlimitsbasedonpredictivelikelihoods PaoloVidoni...123 Computer-intensiveconditionalinference G. AlastairYoungandThomasJ. DiCiccio...137 MonteCarlosimulationmethodsforreliabilityestimationandfailure prognostics EnricoZio...151 ListofContributors AlessandroBaldiAntognini JamesM. Ciera DepartmentofStatisticalSciences DepartmentofStatisticalSciences UniversityofBologna UniversityofPadova Bologna,Italy Padova,Italy ThomasJ. DiCiccio GrazianoAretusi DepartmentofSocialStatistics DepartmentofQuantitativeMethods CornellUniversity andEconomicTheory Ithaca,USA UniversityG. d'Annunzio Chieti-Pescara,Italy LaraFontanella DepartmentofQuantitativeMethods RaffaeleArgiento andEconomicTheory CNRIMATI UniversityG. d'Annunzio Milan,Italy Chieti-Pescara,Italy MassimilianoGiorgio PietroBarbieri DepartmentofAerospace Uf? cioQualita' andMechanicalEngineering CernuscosulNaviglio,Italy SecondUniversityofNaples Aversa(CE),Italy AlessandroBarbiero DepartmentofEconomics Niccolo'Grieco BusinessandStatistics A. O. NiguardaCa'Granda UniversityofMilan Milan,Italy Milan,Italy MaurizioGuida MonicaChiogna DepartmentofElectrical DepartmentofStatisticalSciences andInformationEngineering UniversityofPadova UniversityofSalerno Padova,Italy Fisciano(SA),Italy X ListofContributors AlessandraGuglielmi AntonioPievatolo DepartmentofMathematics CNRIMATI PolitecnicodiMilano Milan,Italy Milan,Italy GianpaoloPulcini alsoaf?liatedtoCNRIMATI,Milano IstitutoMotori NationalResearchCouncil(CNR) FrancescaIeva Naples,Italy MOX-DepartmentofMathematics PolitecnicodiMilano ChiaraRomualdi Milan,Italy DepartmentofBiology UniversityofPadova LuigiIppoliti Padova,Italy DepartmentofQuantitativeMethods andEconomicTheory H?avardRue UniversityG. d'Annunzio DepartmentofMathematicalSciences Chieti-Pescara,Italy NorwegianUniversityforScience andTechnology SaraMartino Trondheim,Norway DepartmentofMathematicalSciences NorwegianUniversityforScience PiercesareSecchi andTechnology MOX-DepartmentofMathematics Trondheim,Norway PolitecnicodiMilano Milan,Italy M. So?aMassa DepartmentofStatisticalSciences PaoloVidoni UniversityofPadova DepartmentofStatistics Padova,Italy UniversityofUdine Udine,Italy FulviaMecatti DepartmentofStatistics G.

Introduction to Bio-Ontologies explores the computational background of ontologies. Emphasizing computational and algorithmic issues surrounding bio-ontologies, this self-contained text helps readers understand ontological algorithms and their applications. The first part of the book defines ontology and bio-ontologies. It also explains the importance of mathematical logic for understanding concepts of inference in bio-ontologies, discusses the probability and statistics topics necessary for understanding ontology algorithms, and describes ontology languages, including OBO (the preeminent language for bio-ontologies), RDF, RDFS, and OWL. The second part covers significant bio-ontologies and their applications. The book presents the Gene Ontology; upper-level ontologies, such as the Basic Formal Ontology and the Relation Ontology; and current bio-ontologies, including several anatomy ontologies, Chemical Entities of Biological Interest, Sequence Ontology, Mammalian Phenotype Ontology, and Human Phenotype Ontology. The third part of the text introduces the major graph-based algorithms for bio-ontologies. The authors discuss how these algorithms are used in overrepresentation analysis, model-based procedures, semantic similarity analysis, and Bayesian networks for molecular biology and biomedical applications. With a focus on computational reasoning topics, the final part describes the ontology languages of the Semantic Web and their applications for inference. It covers the formal semantics of RDF and RDFS, OWL inference rules, a key inference algorithm, the SPARQL query language, and the state of the art for querying OWL ontologies. Web ResourceSoftware and data designed to complement material in the text are available on the book's website: http://bio-ontologies-book.org The site provides the R Robo package developed for the book, along with a compressed archive of data and ontology files used in some of the exercises. It also offers teaching/presentation slides and links to other relevant websites. This book provides readers with the foundation to use ontologies as a starting point for new bioinformatics research projects or to support current molecular genetics research projects. By supplying a self-contained introduction to OBO ontologies and the Semantic Web, it bridges the gap between both fields and helps readers see what each can contribute to the analysis and understanding of biomedical data.

This book brings together geometric tools and their applications for Information analysis. It collects current and many uses of in the interdisciplinary fields of Information Geometry Manifolds in Advanced Signal, Image & Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Machine Learning, Speech/sound recognition and natural language treatment which are also substantially relevant for the industry.

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

This book presents established and state-of-the-art methods in Language Technology (including text mining, corpus linguistics, computational linguistics, and natural language processing), and demonstrates how they can be applied by humanities scholars working with textual data. The landscape of humanities research has recently changed thanks to the proliferation of big data and large textual collections such as Google Books, Early English Books Online, and Project Gutenberg. These resources have yet to be fully explored by new generations of scholars, and the authors argue that Language Technology has a key role to play in the exploration of large-scale textual data. The authors use a series of illustrative examples from various humanistic disciplines (mainly but not exclusively from History, Classics, and Literary Studies) to demonstrate basic and more complex use-case scenarios. This book will be useful to graduate students and researchers in humanistic disciplines working with textual data, including History, Modern Languages, Literary studies, Classics, and Linguistics. This is also a very useful book for anyone teaching or learning Digital Humanities and interested in the basic concepts from computational linguistics, corpus linguistics, and natural language processing.

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. 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 learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Rules - the clearest, most explored and best understood form of knowledge representation - are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.

The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years. The book first discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia data mining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledge discovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification and categorization. This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.

The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students.

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire. Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users. Examines the construction and leveraging of mobile sites Describes how to use mobile apps to gather key data about consumers' behavior and preferences Discusses mobile mobs, which can be differentiated as distinct marketplaces-including Apple (R), Google (R), Facebook (R), Amazon (R), and Twitter (R) Provides detailed coverage of mobile analytics via clustering, text, and classification AI software and techniques Mobile devices serve as detailed diaries of a person, continuously and intimately broadcasting where, how, when, and what products, services, and content your consumers desire. The future is mobile-data mining starts and stops in consumers' pockets. Describing how to analyze Wi-Fi and GPS data from websites and apps, the book explains how to model mined data through the use of artificial intelligence software. It also discusses the monetization of mobile devices' desires and preferences that can lead to the triangulated marketing of content, products, or services to billions of consumers-in a relevant, anonymous, and personal manner.

Technology is not just limited to technology companies, it impacts sectors such as healthcare, agriculture, and security. In the last few decades, countries, too, have started developing technologies or integrating technologies into their systems. As a result, all countries, regardless of size, need to understand the management of engineering and technology concepts. Digital Transformations reviews fundamentals and applications through existing and emerging technologies all around the world. Big data availability and the emergence of new tools provide opportunities to detect the emergence of new technologies. Some of the major elements of such analyses include bibliometrics, patent analysis and social network analysis. The authors focus on these three tools and demonstrate their use through applications such as Blockchain, Artificial Intelligence, Robotics, 3D printing, Wireless Power, Autonomous and Electric Driving, and Smart Homes. Through the examination of cases based on emerging technologies, the book provides a spectrum of these recent applications and serves as a reference for professionals, researchers and students on fundamentals of technology utilization tools.

Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naive Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com

"Temporal Information Processing Technology and Its Applications" systematically studies temporal information processing technology and its applications. The book covers following subjects: 1) time model, calculus and logic; 2) temporal data models, semantics of temporal variable now temporal database concepts; 3) temporal query language, a typical temporal database management system: TempDB; 4) temporal extension on XML, workflow and knowledge base; and, 5) implementation patterns of temporal applications, a typical example of temporal application. The book is intended for researchers, practitioners and graduate students of databases, data/knowledge management and temporal information processing. Dr. Yong Tang is a professor at the Computer School, South China Normal University, China.

The three volume set provides a systematic overview of theories and technique on social network analysis. Volume 1 of the set mainly focuses on the structure characteristics, the modeling, and the evolution mechanism of social network analysis. Techniques and approaches for virtual community detection are discussed in detail as well. It is an essential reference for scientist and professionals in computer science.