Outlier Ensembles
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Outlier Ensembles
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.
Outlier Analysis
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.
Towards Extensible and Adaptable Methods in Computing
This book addresses extensible and adaptable computing, a broad range of methods and techniques used to systematically tackle the future growth of systems and respond proactively and seamlessly to change. The book is divided into five main sections: Agile Software Development, Data Management, Web Intelligence, Machine Learning and Computing in Education. These sub-domains of computing work together in mutually complementary ways to build systems and applications that scale well, and which can successfully meet the demands of changing times and contexts. The topics under each track have been carefully selected to highlight certain qualitative aspects of applications and systems, such as scalability, flexibility, integration, efficiency and context awareness. The first section (Agile Software Development) includes six contributions that address related issues, including risk management, test case prioritization and tools, open source software reliability and predicting the change proneness of software. The second section (Data Management) includes discussions on myriad issues, such as extending database caches using solid-state devices, efficient data transmission, healthcare applications and data security. In turn, the third section (Machine Learning) gathers papers that investigate ML algorithms and present their specific applications such as portfolio optimization, disruption classification and outlier detection. The fourth section (Web Intelligence) covers emerging applications such as metaphor detection, language identification and sentiment analysis, and brings to the fore web security issues such as fraud detection and trust/reputation systems. In closing, the fifth section (Computing in Education) focuses on various aspects of computer-aided pedagogical methods.