Search Results for: Focusing Solutions For Data Mining

Focusing Solutions for Data Mining

Focusing Solutions for Data Mining

Author: Thomas Reinartz

Publisher: Springer

ISBN: 9783540483168

Category: Computers

Page: 316

View: 447

In the first part, this book analyzes the knowledge discovery process in order to understand the relations between knowledge discovery steps and focusing. The part devoted to the development of focusing solutions opens with an analysis of the state of the art, then introduces the relevant techniques, and finally culminates in implementing a unified approach as a generic sampling algorithm, which is then integrated into a commercial data mining system. The last part evaluates specific focusing solutions in various application domains. The book provides various appendicies enhancing easy accessibility. The book presents a comprehensive introduction to focusing in the context of data mining and knowledge discovery. It is written for researchers and advanced students, as well as for professionals applying data mining and knowledge discovery techniques in practice.

Instance Selection and Construction for Data Mining

Instance Selection and Construction for Data Mining

Author: Huan Liu

Publisher: Springer Science & Business Media

ISBN: 9781475733594

Category: Computers

Page: 416

View: 218

The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.

Integration of Data Mining in Business Intelligence Systems

Integration of Data Mining in Business Intelligence Systems

Author: Azevedo, Ana

Publisher: IGI Global

ISBN: 9781466664784

Category: Computers

Page: 314

View: 557

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.

Geographic Data Mining and Knowledge Discovery

Geographic Data Mining and Knowledge Discovery

Author: Harvey J. Miller

Publisher: CRC Press

ISBN: 9781420073980

Category: Computers

Page: 486

View: 193

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has bee

Advances in Data Mining. Applications and Theoretical Aspects

Advances in Data Mining. Applications and Theoretical Aspects

Author: Petra Perner

Publisher: Springer

ISBN: 9783642314889

Category: Computers

Page: 289

View: 445

This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.

Data Mining for Design and Manufacturing

Data Mining for Design and Manufacturing

Author: D. Braha

Publisher: Springer Science & Business Media

ISBN: 9781475749113

Category: Computers

Page: 524

View: 717

Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.

Focusing Solutions for Data Mining

Focusing Solutions for Data Mining

Author: Thomas Reinartz

Publisher: Springer

ISBN: 3540664297

Category: Computers

Page: 316

View: 103

In the first part, this book analyzes the knowledge discovery process in order to understand the relations between knowledge discovery steps and focusing. The part devoted to the development of focusing solutions opens with an analysis of the state of the art, then introduces the relevant techniques, and finally culminates in implementing a unified approach as a generic sampling algorithm, which is then integrated into a commercial data mining system. The last part evaluates specific focusing solutions in various application domains. The book provides various appendicies enhancing easy accessibility. The book presents a comprehensive introduction to focusing in the context of data mining and knowledge discovery. It is written for researchers and advanced students, as well as for professionals applying data mining and knowledge discovery techniques in practice.

Encyclopedia of Computer Science and Technology

Encyclopedia of Computer Science and Technology

Author: Phillip A. Laplante

Publisher: CRC Press

ISBN: 9781351652490

Category: Computers

Page: 1500

View: 299

With breadth and depth of coverage, the Encyclopedia of Computer Science and Technology, Second Edition has a multi-disciplinary scope, drawing together comprehensive coverage of the inter-related aspects of computer science and technology. The topics covered in this encyclopedia include: General and reference Hardware Computer systems organization Networks Software and its engineering Theory of computation Mathematics of computing Information systems Security and privacy Human-centered computing Computing methodologies Applied computing Professional issues Leading figures in the history of computer science The encyclopedia is structured according to the ACM Computing Classification System (CCS), first published in 1988 but subsequently revised in 2012. This classification system is the most comprehensive and is considered the de facto ontological framework for the computing field. The encyclopedia brings together the information and historical context that students, practicing professionals, researchers, and academicians need to have a strong and solid foundation in all aspects of computer science and technology.

Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications

Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications

Author: Rahman El Sheikh, Asim Abdel

Publisher: IGI Global

ISBN: 9781613500514

Category: Computers

Page: 370

View: 538

Business intelligence applications are of vital importance as they help organizations manage, develop, and communicate intangible assets such as information and knowledge. Organizations that have undertaken business intelligence initiatives have benefited from increases in revenue, as well as significant cost savings.Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications highlights the marriage between business intelligence and knowledge management through the use of agile methodologies. Through its fifteen chapters, this book offers perspectives on the integration between process modeling, agile methodologies, business intelligence, knowledge management, and strategic management.

Spatial Data Mining

Spatial Data Mining

Author: Deren Li

Publisher: Springer

ISBN: 9783662485385

Category: Computers

Page: 308

View: 645

· This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.

Managing Bank Risk

Managing Bank Risk

Author: Morton Glantz

Publisher: Academic Press

ISBN: 0122857852

Category: Business & Economics

Page: 692

View: 246

Featuring new credit engineering tools, Managing Bank Risk combines innovative analytic methods with traditional credit management processes. Professor Glantz provides print and electronic risk-measuring tools that ensure credits are made in accordance with bank policy and regulatory requirements, giving bankers with the data necessary for judging asset quality and value. The book's two sections, "New Approaches to Fundamental Analysis" and "Credit Administration," show readers ways to assimilate new tools, such as credit derivatives, cash flow computer modeling, distress prediction and workout, interactive risk rating models, and probabilistic default screening, with well-known controls. By following the guidelines of the Basel Committee on Banking Supervision, Managing Bank Risk offers useful models, programs, and documents essential for creating a sound credit risk environment, credit granting processes, and appropriate administrative and monitoring controls. Key Features * Book includes features such as: * Chapter-concluding questions * Case studies illustrating all major tools * EDF™ Credit Measure provided by KMV, the world's leading provide of market-based quantitative credit risk products * Library of internet links directs readers to information on evolving credit disciplines, such as portfolio management, credit derivatives, risk rating, and financial analysis * CD-ROM containing interactive models and a useful document collection * Credit engineering tools covered include: * Statistics and simulation driven forecasting * Risk adjusted pricing * Credit derivatives * Ratios * Cash flow computer modeling * Distress prediction and workouts * Capital allocation * Credit exposure systems * Computerized loan pricing * Sustainable growth * Interactive risk rating models * Probabilistc default screening * Accompanying CD includes: * Interactive 10-point risk rating model * Comprehensive cash flow model * Trial version of CB Pro, a time-series forecasting program * Stochastic net borrowed funds pricing model * Asset based lending models, courtesy Federal Reserve Bank * The Uniform Financial Institutions Rationg System (CAMELS) * Two portfolio optimization software models * a library of documents from the International Swap Dealers Association, the Basel Committee on Banking Supervision, and others

Strategic Data-Based Wisdom in the Big Data Era

Strategic Data-Based Wisdom in the Big Data Era

Author: Girard, John

Publisher: IGI Global

ISBN: 9781466681231

Category: Business & Economics

Page: 312

View: 312

The ability to uncover, share, and utilize knowledge is one of the most vital components to the success of any organization. While new technologies and techniques of knowledge dissemination are promising, there is still a struggle to derive and circulate meaningful information from large data sets. Strategic Data-Based Wisdom in the Big Data Era combines the latest empirical research findings, best practices, and applicable theoretical frameworks surrounding data analytics and knowledge acquisition. Providing a multi-disciplinary perspective of the subject area, this book is an essential reference source for professionals and researchers working in the field of knowledge management who would like to improve their understanding of the strategic role of data-based wisdom in different types of work communities and environments.