Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language.The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.

This book investigates organizational learning from a variety of information processing perspectives. Continuous change and complexity in regulatory, social and economic environments are increasingly forcing organizations and their employees to acquire the necessary job-specific knowledge at the right time and in the right format. Though many regulatory documents are now available in digital form, their complexity and diversity make identifying the relevant elements for a particular context a challenging task. In such scenarios, business processes tend to be important sources of knowledge, containing rich but in many cases embedded, hidden knowledge. This book discusses the possible connection between business process models and corporate knowledge assets; knowledge extraction approaches based on organizational processes; developing and maintaining corporate knowledge bases; and semantic business process management and its relation to organizational learning approaches. The individual chapters reveal the different elements of a knowledge management solution designed to extract, organize and preserve the knowledge embedded in business processes so as to: enrich organizational knowledge bases in a systematic and controlled way, support employees in acquiring job role-specific knowledge, promote organizational learning, and steer human capital investment. All of these topics are analyzed on the basis of real-world cases from the domains of insurance, food safety, innovation, and funding.

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: * The special characteristics of multi-labeled data and the metrics available to measure them.* The importance of taking advantage of label correlations to improve the results.* The different approaches followed to face multi-label classification.* The preprocessing techniques applicable to multi-label datasets.* The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code". Leslie A. Piegl (Review of the first edition, 2012).

This book reports on advanced theories and cutting-edge applications in the field of soft computing. The individual chapters, written by leading researchers, are based on contributions presented during the 4th World Conference on Soft Computing, held May 25-27, 2014, in Berkeley. The book covers a wealth of key topics in soft computing, focusing on both fundamental aspects and applications. The former include fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation and neural networks, while the latter include soft computing in data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling. By providing readers with a timely, authoritative view on the field, and by discussing thought-provoking developments and challenges, the book will foster new research directions in the diverse areas of soft computing.

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

This is the first textbook on attribute exploration, its theory, its algorithms forapplications, and some of its many possible generalizations. Attribute explorationis useful for acquiring structured knowledge through an interactive process, byasking queries to an expert. Generalizations that handle incomplete, faulty, orimprecise data are discussed, but the focus lies on knowledge extraction from areliable information source.The method is based on Formal Concept Analysis, a mathematical theory ofconcepts and concept hierarchies, and uses its expressive diagrams. The presentationis self-contained. It provides an introduction to Formal Concept Analysiswith emphasis on its ability to derive algebraic structures from qualitative data,which can be represented in meaningful and precise graphics.

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

This book presents the proceedings of the 6th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA-2017), held in Bhubaneswar, Odisha. The event brought together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Information and Decision Sciences, and Intelligent Engineering Informatics. This volume covers broad areas of Information and Decision Sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management & networks, sensor networks, signal processing, wireless networks, protocols & architectures etc. The book also offers a valuable resource for students at the post-graduate level in various engineering disciplines.

This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes. The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics - including process data querying, analysis, matching and correlating process data and models - to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levels of process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications. The book is intended for a broad readership interested in business process management and process analytics. It provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios.

This book constitutes the proceedings of the 15th International Workshop on Algorithms and Models for the Web Graph, WAW 2018, held in Moscow, Russia in May 2018. The 11 full papers presented in this volume were carefully reviewed and selected from various submissions. The papers focus on topics like the information retrieval and data mining on the Web; Web as a text repository and as a graph, induced in various ways by link among pages, hosts and users; the understanding of graphs that arise from the Web and various user activities on the Web; stimulation of the development of high-performance algorithms and applications that exploit these graphs.

With the threats that affect every computer, phone or other device connected to the internet, security has become a responsibility not just for law enforcement authorities or business leaders, but for every individual. Your family, information, property, and business must be protected from cybercriminals in the office, at home, on travel, and in the cloud. Understanding Security Issues provides a solid understanding of the threats, and focuses on useful tips and practices for protecting yourself, all the time, everywhere and anywhere you go. This book discusses security awareness issues and how you can take steps to reduce the risk of becoming a victim: The threats that face every individual and business, all the time. Specific indicators of threats so that you understand when you might be attacked and what to do if they occur. The security mindset and good security practices. Assets that need to be protected at work and at home. Protecting yourself and your business at work. Protecting yourself and your family at home. Protecting yourself and your assets on travel.

The book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Data and Information Systems (ICDIS 2017), held at Indira Gandhi National Tribal University, India from November 3 to 4, 2017. The book covers all aspects of computational sciences and information security. In chapters written by leading researchers, developers and practitioner from academia and industry, it highlights the latest developments and technical solutions, helping readers from the computer industry capitalize on key advances in next-generation computer and communication technology.

This book investigates the ways in which these systems can promote public value by encouraging the disclosure and reuse of privately-held data in ways that support collective values such as environmental sustainability. Supported by funding from the National Science Foundation, the authors' research team has been working on one such system, designed to enhance consumers ability to access information about the sustainability of the products that they buy and the supply chains that produce them. Pulled by rapidly developing technology and pushed by budget cuts, politicians and public managers are attempting to find ways to increase the public value of their actions. Policymakers are increasingly acknowledging the potential that lies in publicly disclosing more of the data that they hold, as well as incentivizing individuals and organizations to access, use, and combine it in new ways. Due to technological advances which include smarter phones, better ways to track objects and people as they travel, and more efficient data processing, it is now possible to build systems which use shared, transparent data in creative ways. The book adds to the current conversation among academics and practitioners about how to promote public value through data disclosure, focusing particularly on the roles that governments, businesses and non-profit actors can play in this process, making it of interest to both scholars and policy-makers.

This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data-we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.

This fundamental guide on programmatic advertising explains in detail how automated, data-driven advertising really works in practice and how the right adoption leads to a competitive advantage for advertisers, agencies and media. The new way of planning, steering and measuring marketing may still appear complex and threatening but promising at once to most decision makers. This collaborative compendium combines proven experience and best practice in 22 articles written by 45 renowned experts from all around the globe. Among them Dr. Florian Heinemann/Project-A, Peter Wurtenberger/Axel-Springer, Deirdre McGlashan/MediaCom, Dr. Marc Grether/Xaxis, Michael Lamb/MediaMath, Carolin Owen/IPG, Stefan Bardega/Zenith, Arun Kumar/Cadreon, Dr. Ralf Strauss/Marketingverband, Jonathan Becher/SAP and many more great minds.

This two-volume set LNCS 10827 and LNCS 10828 constitutes the refereed proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018. The 83 full papers, 21 short papers, 6 industry papers, and 8 demo papers were carefully selected from a total of 360 submissions. The papers are organized around the following topics: network embedding; recommendation; graph and network processing; social network analytics; sequence and temporal data processing; trajectory and streaming data; RDF and knowledge graphs; text and data mining; medical data mining; security and privacy; search and information retrieval; query processing and optimizations; data quality and crowdsourcing; learning models; multimedia data processing; and distributed computing.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.

This book constitutes the refereed proceedings of the 15th Australasian Conference on Data Mining, AusDM 2017, held in Melbourne, VIC, Australia, in August 2017. The 17 revised full papers presented together with 11 research track papers and 6 application track papers were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on clustering and classification; big data; time series; outlier detection and applications; social media and applications.

The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.* Applications: Applications of data mining in domains including databases, social networks, web, bioinformatics, finance, healthcare, and security.* Algorithms: Data mining and machine learning foundations, algorithms, models, and theory.* Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.* Framework: Data mining frameworks, platforms and systems implementation.* Visualizations: Data visualization and modeling.

Power BI Data Analysis and Visualization provides a roadmap to vendor choices and highlights why Microsoft's Power BI is a very viable, cost effective option for data visualization. The book covers the fundamentals and most commonly used features of Power BI, but also includes an in-depth discussion of advanced Power BI features such as natural language queries; embedding Power BI dashboards; and live streaming data. It discusses real solutions to extract data from the ERP application, Microsoft Dynamics CRM, and also offers ways to host the Power BI Dashboard as an Azure application, extracting data from popular data sources like Microsoft SQL Server and open-source PostgreSQL. Authored by Microsoft experts, this book uses real-world coding samples and screenshots to spotlight how to create reports, embed them in a webpage, view them across multiple platforms, and more. Business owners, IT professionals, data scientists, and analysts will benefit from this thorough presentation of Power BI and its functions.

The two-volume set LNCS 9623 + 9624 constitutes revised selected papers from the CICLing 2016 conference which took place in Konya, Turkey, in April 2016. The total of 89 papers presented in the two volumes was carefully reviewed and selected from 298 submissions. The book also contains 4 invited papers and a memorial paper on Adam Kilgarriff's Legacy to Computational Linguistics. The papers are organized in the following topical sections: Part I: In memoriam of Adam Kilgarriff; general formalisms; embeddings, language modeling, and sequence labeling; lexical resources and terminology extraction; morphology and part-of-speech tagging; syntax and chunking; named entity recognition; word sense disambiguation and anaphora resolution; semantics, discourse, and dialog. Part II: machine translation and multilingualism; sentiment analysis, opinion mining, subjectivity, and social media; text classification and categorization; information extraction; and applications.