Feature Selection For Knowledge Discovery And Data Mining

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Feature Selection for Knowledge Discovery and Data Mining

Huan Liu,Hiroshi Motoda
Feature Selection for Knowledge Discovery and Data Mining Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : Springer Science & Business Media
Page : 225 pages
File Size : 41,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461556893

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Feature Selection for Knowledge Discovery and Data Mining by Huan Liu,Hiroshi Motoda Pdf

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.

Computational Methods of Feature Selection

Huan Liu,Hiroshi Motoda
Computational Methods of Feature Selection Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : CRC Press
Page : 440 pages
File Size : 47,9 Mb
Release : 2007-10-29
Category : Computers
ISBN : 1584888792

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Computational Methods of Feature Selection by Huan Liu,Hiroshi Motoda Pdf

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool. The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.

Spectral Feature Selection for Data Mining (Open Access)

Zheng Alan Zhao,Huan Liu
Spectral Feature Selection for Data Mining Open Access Book PDF

Author : Zheng Alan Zhao,Huan Liu
Publisher : CRC Press
Page : 224 pages
File Size : 42,6 Mb
Release : 2011-12-14
Category : Business & Economics
ISBN : 9781439862100

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Spectral Feature Selection for Data Mining (Open Access) by Zheng Alan Zhao,Huan Liu Pdf

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise

Hierarchical Feature Selection for Knowledge Discovery

Cen Wan
Hierarchical Feature Selection for Knowledge Discovery Book PDF

Author : Cen Wan
Publisher : Springer
Page : 120 pages
File Size : 46,8 Mb
Release : 2018-11-29
Category : Computers
ISBN : 9783319979199

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Hierarchical Feature Selection for Knowledge Discovery by Cen Wan Pdf

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Feature Selection for Knowledge Discovery and Data Mining

Subramanian Appavu alias Balamurugan
Feature Selection for Knowledge Discovery and Data Mining Book PDF

Author : Subramanian Appavu alias Balamurugan
Publisher : LAP Lambert Academic Publishing
Page : 60 pages
File Size : 46,8 Mb
Release : 2012
Category : Electronic
ISBN : 3659166928

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Feature Selection for Knowledge Discovery and Data Mining by Subramanian Appavu alias Balamurugan Pdf

With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods such as Bayes Feature Selector, Class Association rule-Information Gain feature selector and Bayes Theorem-Information Gain Feature Selector and compares them using data sets with combination of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines on how to use different methods under various circumstances.

Data Mining and Knowledge Discovery Handbook

Oded Maimon,Lior Rokach
Data Mining and Knowledge Discovery Handbook Book PDF

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1378 pages
File Size : 44,5 Mb
Release : 2006-05-28
Category : Computers
ISBN : 9780387254654

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Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Pdf

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Data Mining Methods for Knowledge Discovery

Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski
Data Mining Methods for Knowledge Discovery Book PDF

Author : Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski
Publisher : Springer Science & Business Media
Page : 508 pages
File Size : 46,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461555896

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Data Mining Methods for Knowledge Discovery by Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski Pdf

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Knowledge Discovery and Data Mining. Current Issues and New Applications

Takao Terano,Huan Liu,Arbee L.P. Chen
Knowledge Discovery and Data Mining Current Issues and New Applications Book PDF

Author : Takao Terano,Huan Liu,Arbee L.P. Chen
Publisher : Springer Science & Business Media
Page : 476 pages
File Size : 47,9 Mb
Release : 2007-07-13
Category : Computers
ISBN : 9783540455714

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Knowledge Discovery and Data Mining. Current Issues and New Applications by Takao Terano,Huan Liu,Arbee L.P. Chen Pdf

The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica tion developers to share their original research results and practical development experiences. A wide range of current KDD topics were covered including ma chine learning, databases, statistics, knowledge acquisition, data visualization, knowledge-based systems, soft computing, and high performance computing. It followed the success of PAKDD 97 in Singapore, PAKDD 98 in Austraha, and PAKDD 99 in China by bringing together participants from universities, indus try, and government from all over the world to exchange problems and challenges and to disseminate the recently developed KDD techniques. This PAKDD 2000 proceedings volume addresses both current issues and novel approaches in regards to theory, methodology, and real world application. The technical sessions were organized according to subtopics such as Data Mining Theory, Feature Selection and Transformation, Clustering, Application of Data Mining, Association Rules, Induction, Text Mining, Web and Graph Mining. Of the 116 worldwide submissions, 33 regular papers and 16 short papers were accepted for presentation at the conference and included in this volume. Each submission was critically reviewed by two to four program committee members based on their relevance, originality, quality, and clarity.

Advances in Knowledge Discovery and Data Mining

Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi
Publisher : Springer
Page : 852 pages
File Size : 44,8 Mb
Release : 2018-06-16
Category : Computers
ISBN : 9783319930404

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Advances in Knowledge Discovery and Data Mining by Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi Pdf

This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018. The 164 full papers were carefully reviewed and selected from 592 submissions. The volumes present papers focusing on new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.

Feature Extraction, Construction and Selection

Huan Liu,Hiroshi Motoda
Feature Extraction Construction and Selection Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : Springer Science & Business Media
Page : 418 pages
File Size : 42,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461557258

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Feature Extraction, Construction and Selection by Huan Liu,Hiroshi Motoda Pdf

There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.

Advances in Knowledge Discovery and Data Mining

Jian Pei,Vincent S. Tseng,Longbing Cao,Hiroshi Motoda,Guandong Xu
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Jian Pei,Vincent S. Tseng,Longbing Cao,Hiroshi Motoda,Guandong Xu
Publisher : Springer
Page : 610 pages
File Size : 54,7 Mb
Release : 2013-04-06
Category : Computers
ISBN : 9783642374531

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Advances in Knowledge Discovery and Data Mining by Jian Pei,Vincent S. Tseng,Longbing Cao,Hiroshi Motoda,Guandong Xu Pdf

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.

Feature Engineering for Machine Learning and Data Analytics

Guozhu Dong,Huan Liu
Feature Engineering for Machine Learning and Data Analytics Book PDF

Author : Guozhu Dong,Huan Liu
Publisher : CRC Press
Page : 389 pages
File Size : 54,7 Mb
Release : 2018-03-14
Category : Business & Economics
ISBN : 9781351721264

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Feature Engineering for Machine Learning and Data Analytics by Guozhu Dong,Huan Liu Pdf

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Knowledge Discovery and Data Mining

O. Maimon,M. Last
Knowledge Discovery and Data Mining Book PDF

Author : O. Maimon,M. Last
Publisher : Springer Science & Business Media
Page : 169 pages
File Size : 55,7 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9781475732962

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Knowledge Discovery and Data Mining by O. Maimon,M. Last Pdf

This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).

Advances in Knowledge Discovery and Data Mining

Tru Cao,Ee-Peng Lim,Zhi-Hua Zhou,Tu-Bao Ho,David Cheung,Hiroshi Motoda
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Tru Cao,Ee-Peng Lim,Zhi-Hua Zhou,Tu-Bao Ho,David Cheung,Hiroshi Motoda
Publisher : Springer
Page : 773 pages
File Size : 48,8 Mb
Release : 2015-05-08
Category : Computers
ISBN : 9783319180328

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Advances in Knowledge Discovery and Data Mining by Tru Cao,Ee-Peng Lim,Zhi-Hua Zhou,Tu-Bao Ho,David Cheung,Hiroshi Motoda Pdf

This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015. The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional, and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations.

Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

Evangelos Triantaphyllou,Giovanni Felici
Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques Book PDF

Author : Evangelos Triantaphyllou,Giovanni Felici
Publisher : Springer Science & Business Media
Page : 784 pages
File Size : 53,9 Mb
Release : 2006-09-10
Category : Computers
ISBN : 9780387342962

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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques by Evangelos Triantaphyllou,Giovanni Felici Pdf

This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.