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检索条件"丛书名=Chapman & Hall/CRC Data Mining and Knowledge Discovery Series"

共 43 条记录，以下是11-20 订阅

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Accelerating discovery: mining Unstructured Information for Hypothesis Generation

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丛书名： chapman & hall/crc data mining and knowledge discovery series

2015年

作者： Scott Spangler

ISBN: (纸本)1482239132;9781482239133

Unstructured mining Approaches to Solve Complex Scientific Problems As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating discovery: mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses. The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches. Encompassing systematic and practical perspectives, the book provides the necessary motivation and strategies as well as a heterogeneous set of comprehensive, illustrative examples. It reveals the importance of heterogeneous data analytics in aiding scientific discoveries and furthers data science as a discipline.

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knowledge discovery for Counterterrorism and Law Enforcement (chapman & hall/crc data mining and knowledge discovery series) 1

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丛书名： chapman & hall/crc data mining and knowledge discovery series

2008年

作者： David Skillicorn

ISBN: (数字)9780429142499

ISBN: (纸本)9781420073997

Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. knowledge discovery for Counterterrorism and Law Enforcement operates from the premise that detection algorithms must be rethought to be effective in this domain, and presents a new approach based on cutting-edge analysis for use in adversarial settings. Reveals How Criminals Conceal Information This volume focuses on four main forms of knowledge discovery: prediction, clustering, relationship discovery, and textual analysis. For each of these application areas, the author discusses opportunities for concealment that are available to criminals and reveals some of the tactics that can aid in detecting them. He reviews what is known about the different technologies for each area and evaluates their effectiveness. The book also supplies a preview of technologies currently under development and describes how they will fit in to existing approaches to knowledge discovery. Provides Proactive Formulas for Staying One Step Ahead of Adversaries While all knowledge-discovery systems are susceptible to manipulation, designers and users of algorithmic systems who are armed with the knowledge of these subversive tactics are better able to create systems to avoid these vulnerabilities. This book delineates an effective process for integrating knowledge-discovery tools, provides a unique understanding of the limits of the technology, and contains a clear presentation of the upsides and pitfalls of data collection. It is a powerful weapon in the arsenal of anyone confronting the increasingly sophisticated tactics employed by lawbreakers and other unsavory individuals.

关键词： Computer Science Politics & International Relations Social Sciences

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data Classification: Algorithms and Applications 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2014年

作者： Charu C. Aggarwal

ISBN: (数字)9781466586758

ISBN: (纸本)9781466586741

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, data Classification: Algorithms and Applications explores the underlyi

关键词： data Preparation \u0026 mining Machine Learning

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data Clustering: Algorithms and Applications 0

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Charu C. Aggarwal Chandan K. Reddy

ISBN: (纸本)9781466558229;9781466558212

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering processincluding how to verify the quality of the underlying clustersthrough supervision, human intervention, or the automated generation of alternative clusters.

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Practical Graph mining with R

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丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Samatova, Nagiza F. Hendrix, William Jenkins, John Padmanabhan, Kanchana Chakraborty, Arpan

ISBN: (数字)9781439860854

ISBN: (纸本)9781439860847;9781439860854

Discover Novel and Insightful knowledge from data Represented as a Graph Practical Graph mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph data mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique. Makes Graph mining Accessible to Various Levels of Expertise Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.

关键词： Computer Science

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RapidMiner: data mining Use Cases and Business Analytics Applications

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Markus Hofmann Ralf Klinkenberg

ISBN: (纸本)9781482205503;9781482205497

Powerful, Flexible Tools for a data-Driven WorldAs the data deluge continues in todays world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: data mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at ***. Understand Each Stage of the data mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve

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Support vector machines : optimization based theory, algorithms, and extensions /

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2013年

作者： Deng Naiyang.

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Foundations of Predictive Analytics 1

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丛书名： chapman and hall/crc data mining and knowledge discovery Ser.

2012年

作者： James Wu Stephen Coggeshall

ISBN: (数字)9781439869482

ISBN: (纸本)9781439869468

Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety

关键词： data mining Predictive control Automatic control

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Computational Business Analytics 1

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Subrata Das

ISBN: (数字)9780429066344

ISBN: (纸本)9781439890738;9781439890707

Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagrams Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.

关键词： Economics, Finance, Business & Industry Mathematics & Statistics

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Computational intelligent data analysis for sustainable development

引用

丛书名： chapman & hall/crc data mining and knowledge discovery series

2013年

作者： Ting Yu Nitesh Chawla Simeon J Simoff

ISBN: (纸本)9781439895948;9781138198692;1439895945

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates t

关键词： Public works Social policy Environmental quality data mining Sustainable development

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