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This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
This book constitutes the thoroughly refereed short papers, workshops and doctoral consortium papers of the 21th European Conference on Advances in Databases and Information Systems, ADBIS 2017, held in Nicosia, Cyprus, in September 2017. The 25 full and 4 short workshop papers and the 12 short papers of the main conference were carefully reviewed and selected from 160 submissions. The papers from the following workshops have been included in the proceedings: the first workshop on Data-Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017; the first workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017; the first international workshop on Data Science: Methodologies and Use-Cases, DaS 2017; the second international workshop on Semantic Web for Cultural Heritage, SW4CH 2017.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
This volume gathers selected, peer-reviewed original contributions presented at the International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC) conference which was held in Coimbatore, India, on November 29-30, 2018. The works included here offer a rich and diverse sampling of recent developments in the fields of Computational Vision, Fuzzy, Image Processing and Bio-inspired Computing. The topics covered include computer vision; cryptography and digital privacy; machine learning and artificial neural networks; genetic algorithms and computational intelligence; the Internet of Things; and biometric systems, to name but a few. The applications discussed range from security, healthcare and epidemic control to urban computing, agriculture and robotics. In this book, researchers, graduate students and professionals will find innovative solutions to real-world problems in industry and society as a whole, together with inspirations for further research.
The State of the Art in Intrusion Prevention and Detection analyzes the latest trends and issues surrounding intrusion detection systems in computer networks, especially in communications networks. Its broad scope of coverage includes wired, wireless, and mobile networks; next-generation converged networks; and intrusion in social networks.Presenti
This book constitutes refereed proceedings of the 4th International Conference on New Trends in Information and Communications Technology Applications, NTICT 2020, held on June 15, 2020. The NTICT conference was planned to take place in Baghdad on March 11-12, 2019, but due to the COVID-19 pandemic the conference has been postponed on June 15, 2020 and moved to the virtual format. The 15 full papers and 3 short papers presented were thoroughly reviewed and selected from 90 qualified submissions. The volume presents the latest research results in such areas as network protocols, overlay and other logical network structures, wireless access networks, computer vision, machine learning, artificial Intelligence, data mining, control methods.
This book constitutes the refereed proceedings of the 5th International Conference on Information, Communication and Computing Technology, ICICCT 2020, held in New Delhi, India*, in May 2020. The 24 full papers and one short paper presented in this volume were carefully reviewed and selected from 220 submissions. The papers are organized in topical sections on data communication & networking; advanced computing using machine learning. *The conference was held virutally due to the COVID-19 pandemic.
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 constitutes the refereed proceedings of the 15th International Conference on Similarity Search and Applications, SISAP 2022, held in Bologna, Italy in October 2022. SISAP 2022 is an annual international conference for researchers focusing on similarity search challenges and related theoretical/practical problems, as well as the design of content-based similarity search applications. The 15 full papers presented together with 8 short and 2 doctoral symposium papers were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Applications; Foundations; Indexing and Clustering; Learning; Doctoral Symposium.
The International Conference on Intelligent Computing (ICIC) was set up as an annual forum dedicated to emerging and challenging topics in the various aspects of advances in computational intelligence fields, such as artificial intelligence, machine learning, bioinformatics, and computational biology, etc. The goal of this conference was to bring together researchers from academia and industry as well as practitioners to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. This book constitutes the proceedings of the International Conference on Intelligent Computing (ICIC 2005), held in Hefei, Anhui, China, during August 23–26, 2005. ICIC 2005 received over 2000 submissions from authors in 39 countries and regions. Based on rigorous peer reviews, the Program Committee selected 563 high-quality papers for presentation at ICIC 2005; of these, 215 papers were published in this book organized into 9 categories, and the other 348 papers were published in five international journals. The organizers of ICIC 2005 made great efforts to ensure the success of this conference. We here thank the members of the ICIC 2005 Advisory Committee for their guidance and advice, the members of the Program Committee and the referees for reviewing the papers, and the members of the Publication Committee for checking and compiling the papers. We would also like to thank the publisher, Springer, for their support in publishing the proceedings in the Lecture Notes in Computer Science series. Particularly, we would like to thank all the authors for contributing their papers.