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Robust Data Mining

Robust Data Mining
Author: Petros Xanthopoulos
Publisher: Springer Science & Business Media
Total Pages: 67
Release: 2012-11-28
Genre: Mathematics
ISBN: 1441998780
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Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.

Robust Data Mining

Robust Data Mining
Author: Springer
Publisher:
Total Pages: 72
Release: 2012-11-26
Genre:
ISBN: 9781441998798
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Robust Data Mining Techniques with Application in Biomedicine and Engineering

Robust Data Mining Techniques with Application in Biomedicine and Engineering
Author: Petros Xanthopoulos
Publisher:
Total Pages:
Release: 2011
Genre:
ISBN:
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ABSTRACT: Analysis and interpretation of large datasets is a very significant problem that arises in many areas of science. The task of data analysis becomes even harder when data are uncertain or imprecise. Such uncertainties introduce bias and make massive data analysis an even more challenging task. Over the years there have been developed many mathematical methodologies for data analysis based on mathematical programming. In this field, the deterministic approach for handling uncertainty and immunizing algorithms against undesired scenarios is robust optimization. In this work we examine the application of robust optimization is some well known data mining algorithms. We explore the optimization structure of such algorithms and then we state their robust counterpart formulation.

Robust Representation for Data Analytics

Robust Representation for Data Analytics
Author: Sheng Li
Publisher: Springer
Total Pages: 229
Release: 2017-08-09
Genre: Computers
ISBN: 3319601768
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This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Robust Statistics

Robust Statistics
Author: Ricardo A. Maronna
Publisher: John Wiley & Sons
Total Pages: 466
Release: 2019-01-04
Genre: Mathematics
ISBN: 1119214688
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A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

Understanding Robust and Exploratory Data Analysis

Understanding Robust and Exploratory Data Analysis
Author: David C. Hoaglin
Publisher: John Wiley & Sons
Total Pages: 484
Release: 2000-06-02
Genre: Mathematics
ISBN: 0471384917
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Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant methods in statistical practice.

Multidimensional Mining of Massive Text Data

Multidimensional Mining of Massive Text Data
Author: Chao Zhang
Publisher: Morgan & Claypool Publishers
Total Pages: 199
Release: 2019-03-21
Genre: Computers
ISBN: 1681735202
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Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
Total Pages: 740
Release: 2011-06-09
Genre: Computers
ISBN: 0123814804
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data