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This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010, held in Adelaide, Australia, in December 2010. The 52 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on knowledge representation and reasoning; data mining and knowledge discovery; machine learning; statistical learning; evolutionary computation; particle swarm optimization; intelligent agent; search and planning; natural language processing; and AI applications.
This book highlights recent research on intelligent systems and nature-inspired computing. It presents 130 selected papers from the 19th International Conference on Intelligent Systems Design and Applications (ISDA 2020), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.
This book constitutes the refereed proceedings of the 14th Pacific Rim Conference on Artificial Intelligence, PRICAI 2016, held in Phuket, Thailand, in August 2016. The 53 regular papers and 15 short papers presented in this volume were carefully reviewed and selected from 161 submissions. Pricai covers a wide range of topics such as AI foundations; applications of AI; semantic web; information retrieval; constraint satisfaction; multimodal interaction; knowledge representation; social networks; ad-hoc networks; algorithms; software architecture; machine learning; and smart modeling and simulation.
Digital agriculture is an emerging concept of modern farming that refers to managing farms using modern Engineering, Information and Communication Technologies (EICT) aiming at increasing the overall efficiency of agricultural production, improving the quantity and quality of products, and optimizing the human labor required and natural resource consumption in operations. This encyclopedia is designed to collect the summaries of knowledge on as many as subjects or aspects relevant to ECIT for digital agriculture, present such knowledge in entries, and arrange them alphabetically by articles titles. Springer Major Reference Works platform offers Live Update capability. Our reference work takes full advantage of this feature, which allows for continuous improvement or revision of published content electronically. The Editorial Board Dr. Irwin R. Donis-Gonzalez, University of California Davis, Dept. Biological and Agricultural Engineering, Davis, USA (Section: Postharvest Technologies) Prof. Paul Heinemann, Pennsylvania State University, Department Head of Agricultural and Biological Engineering, PA, USA (Section: Technologies for Crop Production) Prof. Manoj Karkee, Washington State University, Center for Precision and Automated Agricultural Systems, Washington, USA (Section: Robotics and Automation Technologies) Prof. Minzan Li, China Agricultural University, Beijing, China (Section: Precision Agricultural Technologies) Prof. Dikai Liu, University of Technology Sydney (UTS),Faculty of Engineering & Information Technologies, Broadway NSW, Australia (Section: AI, Information and Communication Technologies) Prof. Tomas Norton, University of Leuven, Dept. of Biosystems, Heverlee Leuven, Belgium (Section: Technologies for Animal and Aquatic Production) Dr. Manuela Zude-Sasse, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Precision Horticulture, Potsdam, Germany (Section: Engineering and Mechanization Technologies)
This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.