Robust Data Mining

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Robust Data Mining

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Author : Petros Xanthopoulos
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-28

Robust Data Mining written by Petros Xanthopoulos and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-28 with Mathematics categories.

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

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Author : Springer
language : en
Publisher:
Release Date : 2012-11-26

Robust Data Mining written by Springer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-26 with categories.

Robust Latent Feature Learning For Incomplete Big Data

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Author : Di Wu
language : en
Publisher: Springer Nature
Release Date : 2022-12-06

Robust Latent Feature Learning For Incomplete Big Data written by Di Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.

Robust Representation For Data Analytics

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Author : Sheng Li
language : en
Publisher: Springer
Release Date : 2017-08-09

Robust Representation For Data Analytics written by Sheng Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with Computers categories.

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.

A Robust Fuzzy Based Clustering Approach In Data Mining Using On Call Data Records

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Author : Independently Published
language : en
Publisher:
Release Date : 2017-08-09

A Robust Fuzzy Based Clustering Approach In Data Mining Using On Call Data Records written by Independently Published and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with categories.

Information mining, the extraction of concealed prognostic information from mammoth databases, might be an intense new innovation with decent potential to help firms represent considerable authority in the chief fundamental data in their insight stores. information preparing devices anticipate future patterns and practices, allowing organizations to make proactive, learning driven decisions

Multidimensional Mining Of Massive Text Data

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Author : Chao Zhang
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2019-03-21

Multidimensional Mining Of Massive Text Data written by Chao Zhang and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-21 with Computers categories.

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.

Robust Cluster Analysis And Variable Selection

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Author : Gunter Ritter
language : en
Publisher: CRC Press
Release Date : 2014-09-02

Robust Cluster Analysis And Variable Selection written by Gunter Ritter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-02 with Computers categories.

Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals. Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.

Data Mining And Business Analytics With R

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Author : Johannes Ledolter
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28

Data Mining And Business Analytics With R written by Johannes Ledolter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Mathematics categories.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Advances In Data Mining Applications And Theoretical Aspects

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Author : Petra Perner
language : en
Publisher: Springer
Release Date : 2013-07-11

Advances In Data Mining Applications And Theoretical Aspects written by Petra Perner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-11 with Computers categories.

This book constitutes the refereed proceedings of the 13th Industrial Conference on Data Mining, ICDM 2013, held in New York, NY, in July 2013. The 22 revised full papers presented were carefully reviewed and selected from 112 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, finance and telecommunication, in medicine and agriculture, and in process control, industry and society.

Knowledge Discovery Process And Methods To Enhance Organizational Performance

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Author : Kweku-Muata Osei-Bryson
language : en
Publisher: CRC Press
Release Date : 2015-03-16

Knowledge Discovery Process And Methods To Enhance Organizational Performance written by Kweku-Muata Osei-Bryson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-16 with Business & Economics categories.

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id