Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event...
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ISBN:
(数字)9781466568594
ISBN:
(纸本)9781466568570
Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing sys
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 in...
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, datamining, 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.
Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledgediscovery. Unfortunately, knowledge discov...
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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 knowledgediscovery. Unfortunately, knowledgediscovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. knowledgediscovery 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 knowledgediscovery: 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 knowledgediscovery. 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.
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, datamining, and machine learning. Addres...
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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, datamining, and machine learning. Addressing the work of these different communities in a unified way, data Classification: Algorithms and Applications explores the underlyi
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, datamining, and machine learning communities. Addressing this problem in a unified way, data Clustering: Algorith...
ISBN:
(纸本)9781466558229;9781466558212
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, datamining, 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.
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 ...
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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 datamining 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 datamining, this self-contained book is accessible to students, researchers, and practitioners of graph datamining. It is suitable as a primary textbook for graph mining or as a supplement to a standard datamining 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.
Powerful, Flexible Tools for a data-Driven WorldAs the data deluge continues in todays world, the need to master datamining, predictive analytics, and business analytics has never been greater. These techniques and t...
ISBN:
(纸本)9781482205503;9781482205497
Powerful, Flexible Tools for a data-Driven WorldAs the data deluge continues in todays world, the need to master datamining, 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 datamining community, including the developers of the RapidMiner software, RapidMiner: datamining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of datamining 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 datamining ProcessThe book and software tools cover all relevant steps of the datamining 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 datamining 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 datamining applications, enabling you to effectively solve
Drawing on the authors' two decades of experience in applied modeling and datamining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for ma...
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ISBN:
(数字)9781439869482
ISBN:
(纸本)9781439869468
Drawing on the authors' two decades of experience in applied modeling and datamining, 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
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 m...
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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.
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