Charu Aggarwal's Books

Outlier Analysis

By: Charu C. Aggarwal

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With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions– the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

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457

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Springer Science & Business Media

Published Date

ISBN 10

1461463963

ISBN 13

9781461463962

Outlier Ensembles

An Introduction

By: Charu C. Aggarwal,Saket Sathe

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Outlier Ensembles

An Introduction

By: Charu C. Aggarwal,Saket Sathe

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.

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288

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Springer

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ISBN 10

3319547658

ISBN 13

9783319547657

Machine Learning for Text

By: Charu C. Aggarwal

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Machine Learning for Text

By: Charu C. Aggarwal

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.

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583

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Springer Nature

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ISBN 10

3030966232

ISBN 13

9783030966232

Neural Networks and Deep Learning

A Textbook

By: Charu C. Aggarwal

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Neural Networks and Deep Learning

A Textbook

By: Charu C. Aggarwal

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

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512

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Springer

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ISBN 10

3319944630

ISBN 13

9783319944630

Data Mining

The Textbook

By: Charu C. Aggarwal

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Data Mining

The Textbook

By: Charu C. Aggarwal

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago

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746

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Springer

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ISBN 10

3319141422

ISBN 13

9783319141428

100 Splendid Voices

Empowering Womanhood

By: Charu Sharma,Jennis Joy Jacob

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100 Splendid Voices

Empowering Womanhood

By: Charu Sharma,Jennis Joy Jacob

100 Splendid Voices is a book which gives a strong voice to the females of this world. This book contains 100 Female writers from across the Globe which talks about few social issues of this society, home and problems that women's face these days in this world. This book is a collection of Thoughts of wonderful and amazing writers from not only from one country but also from different countries.

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288

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The World Of Hidden Thoughts

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ISBN 10

1704340756

ISBN 13

9781704340753

Recommender Systems

The Textbook

By: Charu C. Aggarwal

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Recommender Systems

The Textbook

By: Charu C. Aggarwal

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

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518

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Springer

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ISBN 10

3319296590

ISBN 13

9783319296593

The Different Bride

By: Charu Singh

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The Different Bride

By: Charu Singh

It’s not everyday that we come across stories like that of Chandrika and Prakash. In a world where it’s not easy to be different, Chandrika does not carry her supposed ‘flaw’ as baggage. She instead lives a happy and cheerful life with clear and serious goals. She works hard towards getting into a top business school, to turn her dreams into reality. Despite her endearing nature, deep within her heart she truly believes that love is not for her. But one fine day, the love bell rings, thanks to Graham Bell! Prakash works as a sales executive with an internet company.He has a strained relationship with his father because of his father’s rigid rules and rigorous expectations. When the two meet by fateful design,it’s not just Chandrika’s beliefs that are set to change but her entire life! The Different Bride is a love story of its own kind. It will leave you feeling stronger about how love transcends all the barriers that may come in its way.

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184

Publisher

Notion Press

Published Date

ISBN 10

1638508550

ISBN 13

9781638508557

Path of the Swan: The Maitreya Chronicles

By: Charu Singh

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Path of the Swan: The Maitreya Chronicles

By: Charu Singh

A moment of intense silence followed and then the Rigden spoke up, his voice even: ÔYou have all been called today before the sacred court and from now, in every breath, in every heartbeat, in the shadow of every moment, in the intensity of the thoughtless state, in life, and in not-life, in physical or subtle form, we declare you our emissaries, our sacred envoys to the world of men. Shambala has a task for youÉÕ Lama Ozer and hisÊnovitiate Tashi leave the hidden monastery where they have lived all their livesÊin answer to a call from the legendary kingdom of Shambala received by the lama while deep in trance. Battling the freezing cold and snow of high, mist-laden mountain passes and the many evil forces that thwart their progress, they trek through Sikkim and Tibet to arrive at the Silver Fortress. Here they meet a host of divine and dark celestial beings, including the golden dakini, Yeshe Nam Lha, daughter of the Goddess Tara; Prince A-KarO, heir to the Lha Empire; and Prince Narasimha, heir to the Rigdens and the Shambala legacy. Both the princes are YesheÕs guardians and suitors, and she must travel with them to Earth where it is decreed that her child, Maitreya, the saviour, will be born. But before that happens they have the Asur forces to combat, and the dark prince Arden, who holds Yeshe captive, enthralled by his brooding menace, bewitched by his spell. Drawing richly from the vast pantheon of otherworldly beings that populate the myths of the Mahayana school of Buddhism, Path of the Swan, the first part of the surreally beautiful Tibetan-Buddhist fantasy series The Maitreya Chronicles, is a mesmerizingÊsaga of the battle between celestials and dark forces, and the descent of the celestials to Earth.

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348

Publisher

Hachette India

Published Date

ISBN 10

9350097079

ISBN 13

9789350097076

Data Mining

The Textbook

By: Charu C. Aggarwal

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Data Mining

The Textbook

By: Charu C. Aggarwal

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746

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Springer

Published Date

ISBN 10

3319141422

ISBN 13

9783319141428

Outlier Detection for Temporal Data

By: Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Han

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Outlier Detection for Temporal Data

By: Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Han

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

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110

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Springer Nature

Published Date

ISBN 10

3031019059

ISBN 13

9783031019050

Artificial Intelligence

A Textbook

By: Charu C. Aggarwal

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Artificial Intelligence

A Textbook

By: Charu C. Aggarwal

This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence. The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

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497

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Springer Nature

Published Date

ISBN 10

3030723577

ISBN 13

9783030723576

Social Network Data Analytics

By: Charu C. Aggarwal

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Social Network Data Analytics

By: Charu C. Aggarwal

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

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508

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Springer Science & Business Media

Published Date

ISBN 10

1441984623

ISBN 13

9781441984623

Recommender Systems

The Textbook

By: Charu C. Aggarwal

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Recommender Systems

The Textbook

By: Charu C. Aggarwal

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518

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Springer

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3319296590

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9783319296593

Machine Learning for Text

By: Charu C. Aggarwal

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Machine Learning for Text

By: Charu C. Aggarwal

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583

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Springer Nature

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3030966232

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9783030966232

Outlier Analysis

By: Charu C. Aggarwal

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Outlier Analysis

By: Charu C. Aggarwal

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.

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481

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Springer

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ISBN 10

3319475789

ISBN 13

9783319475783

Neural Networks and Deep Learning

A Textbook

By: Charu C. Aggarwal

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Neural Networks and Deep Learning

A Textbook

By: Charu C. Aggarwal

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

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542

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Springer Nature

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ISBN 10

3031296427

ISBN 13

9783031296420

Linear Algebra and Optimization for Machine Learning

A Textbook

By: Charu C. Aggarwal

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Linear Algebra and Optimization for Machine Learning

A Textbook

By: Charu C. Aggarwal

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

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507

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Springer Nature

Published Date

ISBN 10

3030403440

ISBN 13

9783030403447

Outlier Detection for Temporal Data

By: Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Han

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Outlier Detection for Temporal Data

By: Manish Gupta,Jing Gao,Charu Aggarwal,Jiawei Han

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131

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Morgan & Claypool Publishers

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ISBN 10

162705376X

ISBN 13

9781627053761

Managing and Mining Uncertain Data

By: Charu C. Aggarwal

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Managing and Mining Uncertain Data

By: Charu C. Aggarwal

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

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494

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Springer Science & Business Media

Published Date

ISBN 10

0387096906

ISBN 13

9780387096902

Data Classification

Algorithms and Applications

By: Charu C. Aggarwal

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Data Classification

Algorithms and Applications

By: Charu C. Aggarwal

Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

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707

Publisher

CRC Press

Published Date

ISBN 10

036765914X

ISBN 13

9780367659141

Outlier Ensembles

An Introduction

By: Charu C. Aggarwal,Saket Sathe

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Outlier Ensembles

An Introduction

By: Charu C. Aggarwal,Saket Sathe

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Page Count

288

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Springer

Published Date

ISBN 10

3319547658

ISBN 13

9783319547657

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