Managing and Mining Uncertain Data

Managing and Mining Uncertain Data

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Charu C. Aggarwal

Publisher: Springer Science & Business Media

ISBN: 9780387096902

Category: Computers

Page: 494

View: 606

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Categories: Computers

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 0387096892

Category: Computers

Page: 494

View: 390

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Categories: Computers

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 0387096892

Category: Computers

Page: 494

View: 590

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Categories: Computers

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 0387096906

Category: Computers

Page: 494

View: 610

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Categories: Computers

Managing and Mining Uncertain Data

Managing and Mining Uncertain Data

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 0387096906

Category: Computers

Page: 494

View: 248

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Categories: Computers

The Handbook of Environmental Chemistry

The Handbook of Environmental Chemistry

The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field.

Author: Otto),d1933- Hutzinger

Publisher:

ISBN: OCLC:874256160

Category: Environmental chemistry

Page:

View: 407

Categories: Environmental chemistry

Managing and Mining Sensor Data

Managing and Mining Sensor Data

Scalable Semantic Web Data Management using vertical partitioning. VLDB Conference, 2007. ... Managing and Mining Uncertain Data, Springer, 2009.

Author: Charu C. Aggarwal

Publisher: Springer Science & Business Media

ISBN: 9781461463092

Category: Computers

Page: 534

View: 352

Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.
Categories: Computers

Querying And Mining Uncertain Data Streams

Querying And Mining Uncertain Data Streams

Different from traditional static database management systems (DBMS), ... Moreover, data uncertainty also widely exists in such applications, i.e., ...

Author: Cheqing Jin

Publisher: World Scientific

ISBN: 9789813142923

Category: Computers

Page: 164

View: 769

Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space.This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering.Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields.
Categories: Computers

Scalable Uncertainty Management

Scalable Uncertainty Management

Aggarwal, C.C.: Managing and Mining Uncertain Data. Springer, Heidelberg (2009) 2. Clark, D.E.: Computational methods for probabilistic decision trees.

Author: Amol Deshpande

Publisher: Springer Science & Business Media

ISBN: 9783642159503

Category: Computers

Page: 389

View: 660

This book constitutes the refereed proceedings of the 4th International Conference on Scalable Uncertainty Management, SUM 2010, held in Toulouse, France, in September 2010. The 26 revised full papers presented together with the abstracts of 2 invited talks and 6 “discussant” contributions were carefully reviewed and selected from 32 submissions. The papers cover all areas of managing substantial and complex kinds of uncertainty and inconsistency in data and knowledge, including applications in decision-support systems, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, text mining, and data mining, and consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.
Categories: Computers

Scalable Uncertainty Management

Scalable Uncertainty Management

In: VLDB Workshop on Management of Uncertain Data (2007) 5. Sarma, A.D., Dong, X., Halevy, A.: Uncertainty in data integration. In: Managing and Mining ...

Author: Lluis Godo

Publisher: Springer

ISBN: 9783642043888

Category: Computers

Page: 309

View: 463

This volume contains the papers presented at the Third International Conference on Scalable Uncertainty Management, SUM 2009, in Washington, DC, September 28-30, 2009. It contains 21 technical papers which were selected out of 30 submitted papers in a rigourous reviewing process. The volume also contains extended abstracts of two invited talks. The volume reflects the growing interest in uncertainty and incosistency and aims at bringing together all those interested in the management of uncertainty and inconsistency at large.
Categories: Computers

Ranking Queries on Uncertain Data

Ranking Queries on Uncertain Data

ACM International Conference on Management of Data (SIGMOD'08) (Vancouver, Canada, ... in Managing and Mining Uncertain Data, ed. by C. Aggarwal (Springer,, ...

Author: Ming Hua

Publisher: Springer Science & Business Media

ISBN: 1441993800

Category: Computers

Page: 224

View: 365

Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data. Theoretical and algorithmic results of ranking queries on uncertain data are presented in the last section of this book. Ranking Queries on Uncertain Data is the first book to systematically discuss the problem of ranking queries on uncertain data.
Categories: Computers

Advanced Data Mining and Applications

Advanced Data Mining and Applications

Aggarwal, C.C.: Managing and Mining Uncertain Data. Springer Publishing Company, Incorporated (2009) 3. Bi, J., Zhang, T.: Support Vector Classification ...

Author: Jie Tang

Publisher: Springer

ISBN: 9783642258565

Category: Computers

Page: 419

View: 182

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.
Categories: Computers

Scalable Uncertainty Management

Scalable Uncertainty Management

The theory of belief functions, also known as Dempster-Shafer theory, is a powerful tool for managing and mining uncertain data. The theory was developed by ...

Author: Weiru Liu

Publisher: Springer

ISBN: 9783642403811

Category: Computers

Page: 387

View: 656

This book constitutes the refereed proceedings of the 7th International Conference on Scalable Uncertainty Management, SUM 2013, held in Washington, DC, USA, in September 2013. The 26 revised full papers and 3 revised short papers were carefully reviewed and selected from 57 submissions. The papers cover topics in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information including applications in decision support systems, machine learning, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.
Categories: Computers

Advances in Knowledge Discovery and Data Mining Part I

Advances in Knowledge Discovery and Data Mining  Part I

Aggarwal, C.C.: On Density Based Transforms for uncertain Data Mining. ... Aggarwal, C.C.: Managing and Mining Uncertain Data.

Author: Mohammed J. Zaki

Publisher: Springer Science & Business Media

ISBN: 9783642136566

Category: Computers

Page: 506

View: 553

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes -proceedings (published in time for the respective conference) -post-proceedings (consisting of thoroughly revised final full papers) -research monographs (which may be based on PhD work) More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include -tutorials (textbook-like monographs or collections of lectures given at advanced courses) -state-of-the-art surveys (offering complete and mediated coverage of a topic) -hot topics (introducing emergent topics to the broader community)
Categories: Computers

Data Clustering

Data Clustering

Managing and Mining Uncertain Data, Springer, 2009. [3] C. C. Aggarwal. On density based transforms for uncertain data mining.

Author: Charu C. Aggarwal

Publisher: CRC Press

ISBN: 9781315360416

Category: Business & Economics

Page: 652

View: 189

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 process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Categories: Business & Economics

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Aggarwal, C.C.: Managing and Mining Uncertain Data, vol. 3. Springer, New York (2010) 8. Hewawasam, K.R., Premaratne, K., Shyu, M.L.: Rule mining and ...

Author: Alessandro Antonucci

Publisher: Springer

ISBN: 9783319615813

Category: Computers

Page: 502

View: 213

This book constitutes the refereed proceedings of the 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017, held in Lugano, Switzerland, in July 2017. The 44 revised full papers presented together with 5 abstracts of invited talks were carefully reviewed and selected from 63 submissions and cover topics on analogical reasoning; argumentation; Bayesian networks; belief functions; conditionals; credal sets, credal networks; decision theory, decision making and reasoning under uncertainty; fuzzy sets, fuzzy logic; logics; orthopairs; possibilistic networks; and probabilistic logics, probabilistic reasoning.
Categories: Computers

Advanced Data Mining and Applications

Advanced Data Mining and Applications

Managing and Mining Uncertain Data. Springer, Heidelberg (2009) 2. Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data.

Author: Longbing Cao

Publisher: Springer

ISBN: 9783642173165

Category: Computers

Page: 628

View: 721

With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
Categories: Computers

Information Reuse and Integration in Academia and Industry

Information Reuse and Integration in Academia and Industry

Managing and mining uncertain data. Springer, New York, pp 257–298 Lan Y, Papoian GA (2007) Evolution of complex probability distributions in enzyme ...

Author: Tansel Özyer

Publisher: Springer Science & Business Media

ISBN: 9783709115381

Category: Computers

Page: 306

View: 564

The present work covers the latest developments and discoveries related to information reuse and integration in academia and industrial settings. The need for dealing with the large volumes of data being produced and stored in the last decades and the numerous systems developed to deal with these is increasingly necessary. Not all these developments could have been achieved without the investing large amounts of resources. Over time, new data sources evolve and data integration continues to be an essential and vital requirement. Furthermore, systems and products need to be revised to adapt new technologies and needs. Instead of building these from scratch, researchers in the academia and industry have realized the benefits of reusing existing components that have been well tested. While this trend avoids reinventing the wheel, it comes at the cost of finding the optimum set of existing components to be utilized and how they should be integrated together and with the new non-existing components which are to be developed. These nontrivial tasks have led to challenging research problems in the academia and industry. These issues are addressed in this book, which is intended to be a unique resource for researchers, developers and practitioners.
Categories: Computers

Discovery And Fusion Of Uncertain Knowledge In Data

Discovery And Fusion Of Uncertain Knowledge In Data

Conference on Knowledge Discovery and Data Mining(KDD), 2008, pp. ... the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2013, pp.

Author: Yue Kun

Publisher: World Scientific

ISBN: 9789813227156

Category: Computers

Page: 224

View: 927

Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent systems to support the real world applications. However, the process involves knowledge acquisition, representation, inference and data, Bayesian network (BN) is the key technology plays a key role in knowledge representation, in order to pave way to cope with incomplete, fuzzy data to solve the real-life problems. This book presents Bayesian network as a technology to support data-intensive and incremental learning in knowledge discovery, inference and data fusion in uncertain environment. Contents: IntroductionData-Intensive Learning of Uncertain KnowledgeData-Intensive Inferences of Large-Scale Bayesian NetworksUncertain Knowledge Representation and Inference for Lineage Processing over Uncertain DataUncertain Knowledge Representation and Inference for Tracing Errors in Uncertain DataFusing Uncertain Knowledge in Time-Series DataSummary Readership: Graduate students, researchers and professionals in the field of artificial intelligence/machine learning and information sciences, especially in databases. Keywords: Uncertain Knowledge;Bayesian Network;Data-Intensive Computing;Lineage;Inference;FusionReview: Key Features: Upon the preliminaries of BN (Pearl, 1988), this book establishes the connection between massive/uncertain/dynamic data management and uncertainty in artificial intelligence, specifically taking BN as the knowledge framework; different from the publications (Pearl, 1988; Russel & Norvig, 2010), this book concerns uncertain knowledge representation and corresponding inferences from the data-driven perspective, where we focus on the construction of knowledge models with respect to specific applications; different from the publication (Han, 2011), this book focuses on the critical problem of knowledge engineering specially taking BN as the framework, instead of the previously-unknown patterns by mining dataThis book presents the theoretic conclusions, algorithmic strategies, running examples and empirical studies while emphasizing the soundness in both theoretic/semantic and executive/applicable perspectives of the methods for discovery and fusion of uncertain knowledge in dataThis book is appropriately a reference book for researchers in the fields of massive data analysis, artificial intelligence and knowledge engineering. As well, this book can be also adopted as textbook for graduate students who major in data mining and knowledge discovery, or intelligent data analysis etc.
Categories: Computers

Data Classification

Data Classification

A rule-based classification algorithm for uncertain data. In The Workshp on Management and Mining of Uncertain Data in ICDE, pages 1633–1640, 2009.

Author: Charu C. Aggarwal

Publisher: CRC Press

ISBN: 9781466586758

Category: Business & Economics

Page: 707

View: 447

Comprehensive Coverage of the Entire Area of Classification Research 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 underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
Categories: Business & Economics