Data Mining and Market Intelligence: Implications for Decision Making, Mustapha Akinkunmi
Автор: Mustapha Akinkunmi Название: Data Mining and Market Intelligence: Implications for Decision Making ISBN: 168173320X ISBN-13(EAN): 9781681733203 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 72070.00 T Наличие на складе: Невозможна поставка. Описание: This book is written to address the issues relating to data gathering, data warehousing, and data analysis, all of which are useful when working with large amounts of data. Using practical examples of market intelligence, this book is designed to inspire and inform readers on how to conduct market intelligence by leveraging data and technology, supporting smart decision making. The book explains some suitable methodologies for data analysis that are based on robust statistical methods. For illustrative purposes, the author uses real-life data for all the examples in this book. In addition, the book discusses the concepts, techniques, and applications of digital media and mobile data mining.Hence, this book is a guide tool for policy makers, academics, and practitioners whose areas of interest are statistical inference, applied statistics, applied mathematics, business mathematics, quantitative techniques, and economic and social statistics.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Автор: Zaman, Elhassan Seliaman, Fadzil Название: Handbook Of Research On Trends And Future Directions In Big Data And Web Intelligence ISBN: 1466685050 ISBN-13(EAN): 9781466685055 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 277200.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Big data is a well-trafficked subject in recent IT discourse and does not lack for current research. In fact, there is such a surfeit of material related to big data—and so much of it of questionably reliability, thanks to the high-gloss efforts of savvy tech-marketing gurus—that it can, at times, be difficult for a serious academician to navigate.The Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence cuts through the haze of glitz and pomp surrounding big data and offers a simple, straightforward reference-source of practical academic utility. Covering such topics as cloud computing, parallel computing, natural language processing, and personalized medicine, this volume presents an overview of current research, insight into recent advances, and gaps in the literature indicative of opportunities for future inquiry and is targeted toward a broad, interdisciplinary audience of students, academics, researchers, and professionals in fields of IT, networking, and data-analytics.
Автор: Lior Rokach Название: Data Mining with Decision Trees ISBN: 981459007X ISBN-13(EAN): 9789814590075 Издательство: World Scientific Publishing Цена: 110880.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.
This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.
This book invites readers to explore the many benefits in data mining that decision trees offer:
Self-explanatory and easy to follow when compacted
Able to handle a variety of input data: nominal, numeric and textual
Scales well to big data
Able to process datasets that may have errors or missing values
High predictive performance for a relatively small computational effort
Available in many open source data mining packages over a variety of platforms
Useful for various tasks, such as classification, regression, clustering and feature selection
Автор: Tuffery Название: Data Mining and Statistics for Decision Making ISBN: 0470688297 ISBN-13(EAN): 9780470688298 Издательство: Wiley Рейтинг: Цена: 74920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Manfred Stede, Jodi Schneider Название: Argumentation Mining ISBN: 1681734613 ISBN-13(EAN): 9781681734613 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 97950.00 T Наличие на складе: Невозможна поставка. Описание: Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others.The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity.Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches.Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text.The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a-necessarily subjective-outlook for the field.
Автор: Behera Название: Computational Intelligence in Data Mining ISBN: 9811080542 ISBN-13(EAN): 9789811080548 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The International Conference on "Computational Intelligence in Data Mining" (ICCIDM), after three successful versions, has reached to its fourth version with a lot of aspiration.
Автор: Andreas L. Symeonidis; Pericles A. Mitkas Название: Agent Intelligence Through Data Mining ISBN: 1441937242 ISBN-13(EAN): 9781441937247 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the use of data mining for smarter, more efficient agents, as well as the challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity.
Автор: Abraham Kandel; Mark Last; Horst Bunke Название: Data Mining and Computational Intelligence ISBN: 3790824844 ISBN-13(EAN): 9783790824841 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Many business decisions are made in the absence of complete information about the decision consequences. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").
Название: Computing Predictive Analytics, Business Intelligence, and Economics ISBN: 177188729X ISBN-13(EAN): 9781771887298 Издательство: Taylor&Francis Рейтинг: Цена: 118410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
Автор: Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, Tapan Kumar Panda Название: Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ISBN: 1522520317 ISBN-13(EAN): 9781522520313 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 252250.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presents the latest advances in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.
Автор: Azevedo & Filipe Santos Название: Integration Of Data Mining In Business Intelligence Systems ISBN: 1466664770 ISBN-13(EAN): 9781466664777 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 194040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries.Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.
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