Outlier Analysis de Charu C. Aggarwal

Outlier Analysis

Autor Charu C. Aggarwal

en Limba Engleză Hardback – 11 ian 2013

Subliniem faptul că Outlier Analysis necesită un nivel de experiență solid în informatică, fiind conceput ca un text academic și tehnic ce presupune cunoștințe prealabile de algoritmi și structuri de date. Suntem de părere că această lucrare reprezintă un punct de referință datorită modului în care Charu C. Aggarwal reușește să unifice perspectivele din data mining, machine learning și statistică într-un cadru computațional coerent. Pe linia practică a volumului Outlier Detection: Techniques and Applications, dar cu un focus mult mai extins pe rigoarea teoretică a metodelor de înaltă dimensionalitate, această a doua ediție aduce completări esențiale prin includerea rețelelor neuronale și a metodelor de factorizare a matricelor.

Structura cărții este organizată progresiv, facilitând o înțelegere aprofundată a fenomenului. Primele șapte capitole pun bazele teoretice, explorând modelele probabilistice, liniare și cele bazate pe proximitate. Ulterior, cuprinsul indică o trecere către metode specifice domeniului, de la date de tip text și serii temporale, până la structuri complexe de tip graf sau rețea. Această abordare sistematică oglindește metodologia autorului întâlnită și în alte lucrări fundamentale ale sale, precum Data Clustering sau Neural Networks and Deep Learning, unde complexitatea este descompusă în componente gestionabile. Credem că integrarea capitolului final dedicat aplicațiilor practice și ghidajului pentru specialiști transformă acest volum dintr-un simplu manual într-un instrument de lucru indispensabil pentru cercetarea aplicată în securitate informatică sau analiza fluxurilor de date.

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Specificații

ISBN-13: 9781461463955
ISBN-10: 1461463955
Pagini: 464
Ilustrații: XV, 446 p.
Dimensiuni: 155 x 235 x 29 mm
Greutate: 0.82 kg
Ediția:2013
Editura: Springer
Colecția Springer
Locul publicării:New York, NY, United States

Public țintă

Research

De ce să citești această carte

Recomandăm această carte cercetătorilor și practicienilor din domeniul analizei datelor care doresc o stăpânire completă a metodelor de detectare a anomaliilor. Cititorul câștigă o perspectivă integrată asupra algoritmilor de bază și a aplicațiilor lor în medii complexe, precum datele spațiale sau rețelele. Este un instrument esențial pentru oricine dorește să implementeze sisteme robuste de detectare a fraudelor sau a intruziunilor.

Despre autor

Charu C. Aggarwal este un cercetător de renume în cadrul diviziei de cercetare IBM, obținând doctoratul la MIT în 1996. Cu o carieră marcată de peste 90 de lucrări științifice publicate și peste 50 de brevete de invenție, Aggarwal a fost desemnat de două ori „Master Inventor” la IBM pentru valoarea comercială a inovațiilor sale. Expertiza sa vastă se reflectă în lucrări de referință precum Data Mining și Recommender Systems, fiind recunoscut în special pentru contribuțiile sale în detectarea amenințărilor bioteroriste în timp real în fluxurile de date.

Descriere scurtă

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.

Cuprins

An Introduction to Outlier Analysis.- Probabilistic and Statistical Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-based Outlier Detection.- High-Dimensional Outlier Detection: The Subspace Method.- Supervised Outlier Detection.- Outlier Detection in Categorical, Text and Mixed Attribute Data.- Time Series and Multidimensional Streaming Outlier Detection.- Outlier Detection in Discrete Sequences.- Spatial Outlier Detection.- Outlier Detection in Graphs and Networks.- Applications of Outlier Analysis.

Recenzii

From the book reviews:
“Aggarwal has written a complete survey of the state of the art in anomaly detection. … His book provides a solid frame of reference for those interested in anomaly detection, both researchers and practitioners, no matter whether they are generalists or they are mostly focused on particular applications. All of them can benefit from the broad overview of the field, the nice introductions to many different techniques, and the annotated pointers for further reading that this book provides.” (Fernando Berzal, Computing Reviews, August, 2014)
“This book is an encyclopedia of how to handle outliers. The author introduces various methods to deal with outliers under various conditions, but in a systematic way so that one can easily find what one needs. The writing style is accessible to readers who do not have deep statistical training. … a good reference book for practitioners and researchers who are not experts in outlier analysis, but want to gain a basic understanding of how to do it.” (Hung Hung, Mathematical Reviews, March, 2014)
“This book aims at providing a missing formal view of recent advances in outlier analysis that have been carried out mostly independently in both the computer science and statistics communities. … the book contains a series of carefully created exercises, attempting to make the book useful as a textbook. … All in all, this is an excellent book. … the book seems to be oriented more towards the experienced researcher who will use this book as reference material … .” (Santiago Ontanon, zbMATH, Vol. 1291, 2014)

Caracteristici

Each chapter contains key research content on the topic, case studies, extensive bibliographic notes and the future direction of research in this field Covers applications for credit card fraud, network intrusion detection, law enforcement and more Content is simplified so students and practitioners can also benefit from this book Includes supplementary material: sn.pub/extras

Notă biografică

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”

Textul de pe ultima copertă

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.