Urban Informatics

Urban Informatics
Author :
Publisher : Springer Nature
Total Pages : 941
Release :
ISBN-10 : 9789811589836
ISBN-13 : 9811589836
Rating : 4/5 (36 Downloads)

Synopsis Urban Informatics by : Wenzhong Shi

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.

Knowledge Discovery in the Social Sciences

Knowledge Discovery in the Social Sciences
Author :
Publisher : University of California Press
Total Pages : 263
Release :
ISBN-10 : 9780520339996
ISBN-13 : 0520339991
Rating : 4/5 (96 Downloads)

Synopsis Knowledge Discovery in the Social Sciences by : Xiaoling Shu

Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. Readers will learn to: • appreciate the role of data mining in scientific research • develop an understanding of fundamental concepts of data mining and knowledge discovery • use software to carry out data mining tasks • select and assess appropriate models to ensure findings are valid and meaningful • develop basic skills in data preparation, data mining, model selection, and validation • apply concepts with end-of-chapter exercises and review summaries

Rough Sets and Data Mining

Rough Sets and Data Mining
Author :
Publisher : Springer Science & Business Media
Total Pages : 429
Release :
ISBN-10 : 9781461314615
ISBN-13 : 1461314615
Rating : 4/5 (15 Downloads)

Synopsis Rough Sets and Data Mining by : T.Y. Lin

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others. Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

Knowledge Discovery Process and Methods to Enhance Organizational Performance

Knowledge Discovery Process and Methods to Enhance Organizational Performance
Author :
Publisher : CRC Press
Total Pages : 398
Release :
ISBN-10 : 9781482212389
ISBN-13 : 1482212382
Rating : 4/5 (89 Downloads)

Synopsis Knowledge Discovery Process and Methods to Enhance Organizational Performance by : Kweku-Muata Osei-Bryson

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.

Scientific Data Mining and Knowledge Discovery

Scientific Data Mining and Knowledge Discovery
Author :
Publisher : Springer Science & Business Media
Total Pages : 398
Release :
ISBN-10 : 9783642027888
ISBN-13 : 3642027881
Rating : 4/5 (88 Downloads)

Synopsis Scientific Data Mining and Knowledge Discovery by : Mohamed Medhat Gaber

Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms
Author :
Publisher : Springer Science & Business Media
Total Pages : 272
Release :
ISBN-10 : 9783662049235
ISBN-13 : 3662049236
Rating : 4/5 (35 Downloads)

Synopsis Data Mining and Knowledge Discovery with Evolutionary Algorithms by : Alex A. Freitas

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

Geographic Data Mining and Knowledge Discovery

Geographic Data Mining and Knowledge Discovery
Author :
Publisher : CRC Press
Total Pages : 408
Release :
ISBN-10 : UOM:39015053172154
ISBN-13 :
Rating : 4/5 (54 Downloads)

Synopsis Geographic Data Mining and Knowledge Discovery by : Harvey J. Miller

Advances in automated data collection are creating massive databases and a whole new field, Knowledge Discovery Databases (KDD), has emerged to develop new methods of managing and exploiting them. Geographic Data Mining and Knowledge Discovery is the interrogation of large databases using efficient computational methods. The unique challenges brought about by the storing of massive geographical databases - from high resolution satellite-based systems to data from intelligent transportation systems, for example - has led to the field of Geographical Knowledge Discovery (GKD). Geographic or spatial data mining is the exploration of these geographical information databases. Developed out of contributions to the highly-respected Varenius Project in 1999, this collection will be the definitive volume focusing on GKD and addresses the special challenges to be found in knowledge discovery and data mining from geographic databases.

Methodologies for Knowledge Discovery and Data Mining

Methodologies for Knowledge Discovery and Data Mining
Author :
Publisher : Springer
Total Pages : 566
Release :
ISBN-10 : 9783540489122
ISBN-13 : 3540489126
Rating : 4/5 (22 Downloads)

Synopsis Methodologies for Knowledge Discovery and Data Mining by : Ning Zhong

This book constitutes the refereed proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '99, held in Beijing, China, in April 1999. The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization; causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies.

Knowledge Discovery and Data Mining: Challenges and Realities

Knowledge Discovery and Data Mining: Challenges and Realities
Author :
Publisher : IGI Global
Total Pages : 290
Release :
ISBN-10 : 9781599042541
ISBN-13 : 1599042541
Rating : 4/5 (41 Downloads)

Synopsis Knowledge Discovery and Data Mining: Challenges and Realities by : Zhu, Xingquan

"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Author :
Publisher : CRC Press
Total Pages : 744
Release :
ISBN-10 : 9781439841747
ISBN-13 : 1439841748
Rating : 4/5 (47 Downloads)

Synopsis Advances in Machine Learning and Data Mining for Astronomy by : Michael J. Way

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines