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Quality Measures in Data Mining
This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
Qualità dei Dati : Concetti, Metodi e Tecniche = Data quality: Concepts, Methods and Techniques
Poor data quality can hinder or seriously damage the efficiency and effectiveness of organizations and businesses. The growing awareness of these repercussions has led to important public initiatives such as the promulgation of the "Data Quality Act" in the United States and the Directive 2003/98 of the European Parliament. The authors present a complete and systematic introduction to the wide range of problems related to data quality. The book starts with a detailed description of different dimensions of data quality, such as accuracy, completeness and consistency, and discusses its importance in relation to both different types of data, such as federated data, data present on the web and data with temporal dependencies, which to the different categories in which the data can be classified. The comprehensive description of techniques and methodologies from not only research in the area of data quality but also related areas, such as data mining, probability theory, statistical data analysis and machine learning, provides an excellent introduction to the state of the art. current art. The presentation is complemented by a short description and a critical comparison of practical tools and methodologies, which will help the reader to solve their quality problems.
Projection-Based Clustering through Self-Organization and Swarm Intelligence : Combining Cluster Analysis with the Visualization of High-Dimensional Data
It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining.
Progress in WWW Research and Development ; 10th Asia-Pacific Web Conference, APWeb 2008, Shenyang, China, April 26-28, 2008. Proceedings
This book is organized in topical sections on data mining and knowledge discovery, wireless, sensor networks and grid, XML and query processing and optimization, privacy and security, information extraction, presentation and retrieval, P2P, agent systems, ontology, semantic Web and Web applications, data streams, time series analysis and data mining, Web mining and Web search, as well as workflow and middleware.
Progress in Spatial Data Handling ; 12th International Symposium on Spatial Data Handling
The series of International Symposia on Spatial Data Handling started in Zurich, Switzerland, in 1984. Since then it has evolved into a biennial event of high calibre, at which recent trends and developments in geographic information science are discussed. The proceedings of this series have become an important reference for researchers and students in the field of geography, cartography, and geoinformation.
Progress in pattern recognition, image analysis and applications ; Vol. 4225 ; 11th Iberoamerican Congress on Pattern Recognition, CIARP 2006, Cancún, Mexico, November 14-17, 2006, Proceedings
Constitutes the refereed proceedings of the 11th Iberoamerican Congress on Pattern Recognition, CIARP 2006, held in Cancun, Mexico in November 2006. The 99 revised full papers presented together with three keynote articles were carefully reviewed and selected from 239 submissions.
Progress in pattern recognition, image analysis and applications ; Vol. 3773 : 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005, Proceedings
X CIARP was a forum for scientific research, experience exchange, share of new knowledge and increase in cooperation between research groups in pattern recognition, computer vision and related areas. The 10th Iberoamerican Congress on Pattern Recognition was organized by the Cuban Association for Pattern Recognition (ACRP) and sponsored by the Institute of Cybernetics, Mathematics and Physics (ICIMAF), the Advanced Technologies Application Center (CENATAV), the University of Oriente (UO), the Polytechnic Institute “José A Echevarria” (ISPJAE), the Central University of Las Villas (UCLV), the Ciego de Avila University (UNICA), as well as the Center of Technologies Research on Information and Systems (CITIS-UAEH) in Mexico. The conference was also co-sponsored by the Portuguese Association for Pattern Recognition (APRP), the Spanish Association for Pattern Recognition and Image Analysis (AERFAI), the Special Interest Group of the Brazilian Computer Society (SIGPR-SBC), and the Mexican Association for Computer Vision, Neurocomputing and Robotics (MACVNR). X CIARP was endorsed by the International Association for Pattern Recognition (IAPR).
Progress in pattern recognition, image analysis and applications ; 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Havana, Cuba, September 9-12, 2008. Proceedings
This book is organized in topical sections on signal analysis for characterization and filtering, analysis of shape and texture, analysis of speech and language, data mining, clustering of images and documents, statistical pattern recognition, classification and description of objects, classification and edition, geometric image analysis, neural networks, computer vision, image coding, associative memories and neural networks, interpolation and video tracking, images analysis, music and speech analysis, as well as classifier combination and document filtering.
Progress in pattern recognition, image analysis and applications ; 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007,Valpariso, Chile, November 13-16, 2007, Proceedings
This book constitutes the refereed proceedings of the 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, held in Valparaiso, Chile, November 13-16, 2007. The papers cover ongoing research and mathematical methods for pattern recognition, image analysis, and applications in such diverse areas as computer vision, robotics and remote sensing, industry, health, space exploration, data mining, document analysis, natural language processing and speech recogni.
Progress in artificial intelligence ; 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Virtual Event, September 7–9, 2021, Proceedings
This book constitutes the refereed proceedings of the 20th EPIA Conference on Artificial Intelligence, EPIA 2021, held virtually in September 2021. The 62 full papers and 6 short papers presented were carefully reviewed and selected from a total of 108 submissions. The papers are organized in the following topical sections: artificial intelligence and IoT in agriculture; artificial intelligence and law; artificial intelligence in medicine; artificial intelligence in power and energy systems; artificial intelligence in transportation systems; artificial life and evolutionary algorithms; ambient intelligence and affective environments; general AI; intelligent robotics; knowledge discovery and business intelligence; multi-agent systems: theory and applications; and text mining and applications.
Process planning and scheduling for distributed manufacturing
Process Planning and Scheduling for Distributed Manufacturing focuses on emerging technologies for distributed intelligent decision-making in process planning and dynamic scheduling. As a collection of chapters on state-of-the-art researches in this area, this book presents a review of several key research areas in process planning and scheduling (e.g., adaptive process planning, dynamic scheduling, and process planning and scheduling integration), and provides an in-depth treatment of particular techniques, from function block enabled process planning to agent-based resource scheduling. Each chapter addresses a specific problem domain and offers practical solutions to solve the problem.
Process Mining Workshops ; ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers
This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas.
Process Mining Handbook
Comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. The 17 chapters presented in this volume were organized in the following topical sections: introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing.
Proceedings of the 2021 DigitalFUTURES ; The 3rd International conference on computational design and robotic fabrication (CDRF 2021)
Selected papers from 2021 DigitalFUTURES—The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). The work focuses on novel techniques for computational design and robotic fabrication. The contents make valuable contributions to academic researchers, designers, and engineers in the industry.
Probabilistic Inductive Logic Programming : Theory and Applications
One of the key open questions within arti?cial intelligence is how to combine probability and logic with learning. This question is getting an increased tentioninseveral disciplines suchas knowledg erepresentation, reasoningabout uncertainty, data mining, and machine learning simulateously,This book providesan introduction to this ?eld with an emphasison those methods based on logic programming principles. The book is also the main result of the successful European ISTFET projectno.FP6-508861on Applition of ProbabilisticInductive Logic Programming (APRILII,2004-2007).It was concerned with theory, implementation sand applications of probabilisticinductivelogic programming.
Pro PerformancePoint Server 2007 : Building Business Intelligence Solutions
Pro PerformancePoint Server 2007 is Microsoft's latest product in its line of business intelligence applications, a piece of software that gathers data from corporate databases and delivers it to an end user in a friendly, graphical fashion. PerformancePoint offers the next step in the digitization world. Businesses now have gigabytes upon terabytes of data in databases; there's a need to interpret the data and glean key business insights from it and PerformancePoint.
Privacy-Preserving Data Mining : Models and Algorithms
Privacy Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume also contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy.
Privacy, Security, and Trust in KDD ; 1st ACM SIGKDD International Workshop, PinKDD 2007, San Jose, CA, USA, August 12, 2007, Revised Selected Papers
Vast amounts of data are collected by service providers and system administ- tors, and are available in public information systems. Data mining technologies provide an ideal framework to assist in analyzing such collections for computer security and surveillance-related endeavors. For instance, system administrators can apply data mining to summarize activity patterns in access logs so that potential malicious incidents can be further investigated. Beyond computer - curity, data mining technology supports intelligence gathering and summari- tion for homeland security. For years, and most recently fueled by events such as September 11, 2001, government agencies have focused on developing and applying data mining technologies to monitor terrorist behaviors in public and private data collections. Theapplicationof data mining to person-specifc data raisesseriousconcerns regarding data con?dentiality and citizens’ privacy rights.
Privacy Preserving Data Mining
Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense. Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area.
Privacy in Statistical Databases; CENEX-SDC Project International Conference, PSD 2006, Rome, Italy, December 13-15, 2006, Proceedings
Privacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses.
