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Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: ,
Editor: Springer
ISBN-10: 3319317504
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Advances in Knowledge Discovery and Data Mining by , Book Summary:

This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016. The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.

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Advances in Knowledge Discovery in Databases

Advances in Knowledge Discovery in Databases [Pdf/ePub] eBook Author: Animesh Adhikari,Jhimli Adhikari
Editor: Springer
ISBN-10: 3319132121
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Advances in Knowledge Discovery in Databases by Animesh Adhikari,Jhimli Adhikari Book Summary:

This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.

Download or read Advances in Knowledge Discovery in Databases book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.


Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data [Pdf/ePub] eBook Author: Wesley W. Chu
Editor: Springer Science & Business Media
ISBN-10: 3642408370
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Data Mining and Knowledge Discovery for Big Data by Wesley W. Chu Book Summary:

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Download or read Data Mining and Knowledge Discovery for Big Data book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.


Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: Jinho Kim,Kyuseok Shim,Longbing Cao,Jae-Gil Lee,Xuemin Lin,Yang-Sae Moon
Editor: Springer
ISBN-10: 331957454X
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Advances in Knowledge Discovery and Data Mining by Jinho Kim,Kyuseok Shim,Longbing Cao,Jae-Gil Lee,Xuemin Lin,Yang-Sae Moon Book Summary:

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.

Download or read Advances in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.


Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho
Editor: Springer
ISBN-10: 3642013074
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Advances in Knowledge Discovery and Data Mining by Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho Book Summary:

This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

Download or read Advances in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.


Advanced Techniques in Knowledge Discovery and Data Mining

Advanced Techniques in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: Nikhil Pal
Editor: Springer Science & Business Media
ISBN-10: 9781846281839
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Advanced Techniques in Knowledge Discovery and Data Mining by Nikhil Pal Book Summary:

Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.

Download or read Advanced Techniques in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.


Trends and Applications in Knowledge Discovery and Data Mining

Trends and Applications in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: Mohadeseh Ganji,Lida Rashidi,Benjamin C. M. Fung,Can Wang
Editor: Springer
ISBN-10: 303004503X
Size: 978 kb
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Trends and Applications in Knowledge Discovery and Data Mining by Mohadeseh Ganji,Lida Rashidi,Benjamin C. M. Fung,Can Wang Book Summary:

This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2018, held in conjunction with the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, in Melbourne, Australia, in June 2018. The 32 revised papers presented were carefully reviewed and selected from 46 submissions. The workshops affiliated with PAKDD 2018 include: Workshop on Big Data Analytics for Social Computing, BDASC, Australasian Workshop on Machine Learning for Cyber-security, ML4Cyber, Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining, BDM, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI, and Workshop on Data Mining for Energy Modeling and Optimization, DaMEMO.

Download or read Trends and Applications in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2018, held in conjunction with the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, in Melbourne, Australia, in June 2018. The 32 revised papers presented were carefully reviewed and selected from 46 submissions. The workshops affiliated with PAKDD 2018 include: Workshop on Big Data Analytics for Social Computing, BDASC, Australasian Workshop on Machine Learning for Cyber-security, ML4Cyber, Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining, BDM, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI, and Workshop on Data Mining for Energy Modeling and Optimization, DaMEMO.


Data Mining and Knowledge Discovery for Process Monitoring and Control

Data Mining and Knowledge Discovery for Process Monitoring and Control [Pdf/ePub] eBook Author: Xue Z. Wang
Editor: Springer Science & Business Media
ISBN-10: 1447104218
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Data Mining and Knowledge Discovery for Process Monitoring and Control by Xue Z. Wang Book Summary:

Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

Download or read Data Mining and Knowledge Discovery for Process Monitoring and Control book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.


Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook [Pdf/ePub] eBook Author: Oded Maimon,Lior Rokach
Editor: Springer Science & Business Media
ISBN-10: 038725465X
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Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Book Summary:

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.

Download or read Data Mining and Knowledge Discovery Handbook book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). 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 and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms [Pdf/ePub] eBook Author: Alex A. Freitas
Editor: Springer Science & Business Media
ISBN-10: 3662049236
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Data Mining and Knowledge Discovery with Evolutionary Algorithms by Alex A. Freitas Book Summary:

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

Download or read Data Mining and Knowledge Discovery with Evolutionary Algorithms book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics


Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy [Pdf/ePub] eBook Author: Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Editor: CRC Press
ISBN-10: 1439841748
Size: 677 kb
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Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Book Summary:

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Download or read Advances in Machine Learning and Data Mining for Astronomy book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines


Scientific Data Mining and Knowledge Discovery

Scientific Data Mining and Knowledge Discovery [Pdf/ePub] eBook Author: Mohamed Medhat Gaber
Editor: Springer Science & Business Media
ISBN-10: 3642027881
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Scientific Data Mining and Knowledge Discovery by Mohamed Medhat Gaber Book Summary:

Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

Download or read Scientific Data Mining and Knowledge Discovery book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.


Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends

Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends [Pdf/ePub] eBook Author: Taniar, David,Iwan, Lukman Hakim
Editor: IGI Global
ISBN-10: 1613504756
Size: 411 kb
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Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends by Taniar, David,Iwan, Lukman Hakim Book Summary:

"This book is an updated look at the state of technology in the field of data mining and analytics offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining"--Provided by publisher.

Download or read Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). "This book is an updated look at the state of technology in the field of data mining and analytics offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining"--Provided by publisher.


Data Mining

Data Mining [Pdf/ePub] eBook Author: Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan
Editor: Springer Science & Business Media
ISBN-10: 0387367950
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Data Mining by Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan Book Summary:

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Download or read Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.


Advances in Data Mining. Applications and Theoretical Aspects

Advances in Data Mining. Applications and Theoretical Aspects [Pdf/ePub] eBook Author: Petra Perner
Editor: Springer
ISBN-10: 3319957864
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Advances in Data Mining. Applications and Theoretical Aspects by Petra Perner Book Summary:

This volume constitutes the proceedings of the 18th Industrial Conference on Adances in Data Mining, ICDM 2018, held in New York, NY, USA, in July 2018. The 24 regular papers presented in this book were carefully reviewed and selected from 146 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry, and society.

Download or read Advances in Data Mining. Applications and Theoretical Aspects book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This volume constitutes the proceedings of the 18th Industrial Conference on Adances in Data Mining, ICDM 2018, held in New York, NY, USA, in July 2018. The 24 regular papers presented in this book were carefully reviewed and selected from 146 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry, and society.


Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: Vincent S. Tseng,Tu Bao Ho,Zhi-Hua Zhou,Arbee L.P. Chen,Hung-Yu Kao
Editor: Springer
ISBN-10: 3319066080
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Advances in Knowledge Discovery and Data Mining by Vincent S. Tseng,Tu Bao Ho,Zhi-Hua Zhou,Arbee L.P. Chen,Hung-Yu Kao Book Summary:

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. The 40 full papers and the 60 short papers presented within these proceedings were carefully reviewed and selected from 371 submissions. They cover the general fields of pattern mining; social network and social media; classification; graph and network mining; applications; privacy preserving; recommendation; feature selection and reduction; machine learning; temporal and spatial data; novel algorithms; clustering; biomedical data mining; stream mining; outlier and anomaly detection; multi-sources mining; and unstructured data and text mining.

Download or read Advances in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. The 40 full papers and the 60 short papers presented within these proceedings were carefully reviewed and selected from 371 submissions. They cover the general fields of pattern mining; social network and social media; classification; graph and network mining; applications; privacy preserving; recommendation; feature selection and reduction; machine learning; temporal and spatial data; novel algorithms; clustering; biomedical data mining; stream mining; outlier and anomaly detection; multi-sources mining; and unstructured data and text mining.


Trends and Applications in Knowledge Discovery and Data Mining

Trends and Applications in Knowledge Discovery and Data Mining [Pdf/ePub] eBook Author: U Kang,Ee-Peng Lim,Jeffrey Xu Yu,Yang-Sae Moon
Editor: Springer
ISBN-10: 3319672746
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Trends and Applications in Knowledge Discovery and Data Mining by U Kang,Ee-Peng Lim,Jeffrey Xu Yu,Yang-Sae Moon Book Summary:

This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2017, held in conjunction with PAKDD, the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining in May 2017 in Jeju, South Korea. The 17 revised papers presented were carefully reviewed and selected from 38 submissions. The workshops affiliated with PAKDD 2017 include: Workshop on Machine Learning for Sensory Data Analysis (MLSDA), Workshop on Biologically Inspired Data Mining Techniques (BDM), Pacific Asia Workshop on Intelligence and Security Informatics (PAISI), and Workshop on Data Mining in Business Process Management (DM-BPM).

Download or read Trends and Applications in Knowledge Discovery and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2017, held in conjunction with PAKDD, the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining in May 2017 in Jeju, South Korea. The 17 revised papers presented were carefully reviewed and selected from 38 submissions. The workshops affiliated with PAKDD 2017 include: Workshop on Machine Learning for Sensory Data Analysis (MLSDA), Workshop on Biologically Inspired Data Mining Techniques (BDM), Pacific Asia Workshop on Intelligence and Security Informatics (PAISI), and Workshop on Data Mining in Business Process Management (DM-BPM).


Data Mining in Finance

Data Mining in Finance [Pdf/ePub] eBook Author: Boris Kovalerchuk,Evgenii Vityaev
Editor: Springer Science & Business Media
ISBN-10: 0306470187
Size: 1465 kb
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Data Mining in Finance by Boris Kovalerchuk,Evgenii Vityaev Book Summary:

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

Download or read Data Mining in Finance book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.


Intelligent Technologies for Information Analysis

Intelligent Technologies for Information Analysis [Pdf/ePub] eBook Author: Ning Zhong,Jiming Liu
Editor: Springer Science & Business Media
ISBN-10: 3662079526
Size: 845 kb
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Intelligent Technologies for Information Analysis by Ning Zhong,Jiming Liu Book Summary:

Intelligent Information Technology (iiT) encompasses the theories and ap plications of artificial intelligence, statistical pattern recognition, learning theory, data warehousing, data mining and knowledge discovery, Grid com puting, and autonomous agents and multi-agent systems in the context of today's as well as future IT, such as Electronic Commerce (EC), Business Intelligence (BI), Social Intelligence (SI), Web Intelligence (WI), Knowledge Grid (KG), and Knowledge Community (KC), among others. The multi-author monograph presents the current state of the research and development in intelligent technologies for information analysis, in par ticular, advances in agents, data mining, and learning theory, from both the oretical and application aspects. It investigates the future of information technology (IT) from a new intelligent IT (iiT) perspective, and highlights major iiT-related topics by structuring an introductory chapter and 22 sur vey/research chapters into 5 parts: (1) emerging data mining technology, (2) data mining for Web intelligence, (3) emerging agent technology, ( 4) emerging soft computing technology, and (5) statistical learning theory. Each chapter includes the original work of the author(s) as well as a comprehensive survey related to the chapter's topic. This book will become a valuable source of reference for R&D profession als active in advanced intelligent information technologies. Students as well as IT professionals and ambitious practitioners concerned with advanced in telligent information technologies will appreciate the book as a useful text enhanced by numerous illustrations and examples.

Download or read Intelligent Technologies for Information Analysis book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Intelligent Information Technology (iiT) encompasses the theories and ap plications of artificial intelligence, statistical pattern recognition, learning theory, data warehousing, data mining and knowledge discovery, Grid com puting, and autonomous agents and multi-agent systems in the context of today's as well as future IT, such as Electronic Commerce (EC), Business Intelligence (BI), Social Intelligence (SI), Web Intelligence (WI), Knowledge Grid (KG), and Knowledge Community (KC), among others. The multi-author monograph presents the current state of the research and development in intelligent technologies for information analysis, in par ticular, advances in agents, data mining, and learning theory, from both the oretical and application aspects. It investigates the future of information technology (IT) from a new intelligent IT (iiT) perspective, and highlights major iiT-related topics by structuring an introductory chapter and 22 sur vey/research chapters into 5 parts: (1) emerging data mining technology, (2) data mining for Web intelligence, (3) emerging agent technology, ( 4) emerging soft computing technology, and (5) statistical learning theory. Each chapter includes the original work of the author(s) as well as a comprehensive survey related to the chapter's topic. This book will become a valuable source of reference for R&D profession als active in advanced intelligent information technologies. Students as well as IT professionals and ambitious practitioners concerned with advanced in telligent information technologies will appreciate the book as a useful text enhanced by numerous illustrations and examples.


Evolutionary Computation in Data Mining

Evolutionary Computation in Data Mining [Pdf/ePub] eBook Author: Ashish Ghosh
Editor: Springer
ISBN-10: 3540323589
Size: 1255 kb
Format File: Pdf
Read: 1255

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Evolutionary Computation in Data Mining by Ashish Ghosh Book Summary:

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

Download or read Evolutionary Computation in Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).