Author: Jiawei Han
Publisher: Elsevier
ISBN: 0080475582
Category : Computers
Languages : en
Pages : 772
Book Description
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Data Mining, Southeast Asia Edition
Author: Jiawei Han
Publisher: Elsevier
ISBN: 0080475582
Category : Computers
Languages : en
Pages : 772
Book Description
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Publisher: Elsevier
ISBN: 0080475582
Category : Computers
Languages : en
Pages : 772
Book Description
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence
Author: Trivedi, Shrawan Kumar
Publisher: IGI Global
ISBN: 1522520325
Category : Computers
Languages : en
Pages : 465
Book Description
The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.
Publisher: IGI Global
ISBN: 1522520325
Category : Computers
Languages : en
Pages : 465
Book Description
The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.
Improving Knowledge Discovery through the Integration of Data Mining Techniques
Author: Usman, Muhammad
Publisher: IGI Global
ISBN: 146668514X
Category : Computers
Languages : en
Pages : 418
Book Description
Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.
Publisher: IGI Global
ISBN: 146668514X
Category : Computers
Languages : en
Pages : 418
Book Description
Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.
Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publisher: Springer
ISBN: 3319210246
Category : Computers
Languages : en
Pages : 447
Book Description
This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
Publisher: Springer
ISBN: 3319210246
Category : Computers
Languages : en
Pages : 447
Book Description
This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
ISBN: 0123814804
Category : Computers
Languages : en
Pages : 740
Book Description
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
Publisher: Elsevier
ISBN: 0123814804
Category : Computers
Languages : en
Pages : 740
Book Description
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
Cyber Criminology
Author: Hamid Jahankhani
Publisher: Springer
ISBN: 3319971816
Category : Social Science
Languages : en
Pages : 353
Book Description
This book provides a comprehensive overview of the current and emerging challenges of cyber criminology, victimization and profiling. It is a compilation of the outcomes of the collaboration between researchers and practitioners in the cyber criminology field, IT law and security field. As Governments, corporations, security firms, and individuals look to tomorrow’s cyber security challenges, this book provides a reference point for experts and forward-thinking analysts at a time when the debate over how we plan for the cyber-security of the future has become a major concern. Many criminological perspectives define crime in terms of social, cultural and material characteristics, and view crimes as taking place at a specific geographic location. This definition has allowed crime to be characterised, and crime prevention, mapping and measurement methods to be tailored to specific target audiences. However, this characterisation cannot be carried over to cybercrime, because the environment in which such crime is committed cannot be pinpointed to a geographical location, or distinctive social or cultural groups. Due to the rapid changes in technology, cyber criminals’ behaviour has become dynamic, making it necessary to reclassify the typology being currently used. Essentially, cyber criminals’ behaviour is evolving over time as they learn from their actions and others’ experiences, and enhance their skills. The offender signature, which is a repetitive ritualistic behaviour that offenders often display at the crime scene, provides law enforcement agencies an appropriate profiling tool and offers investigators the opportunity to understand the motivations that perpetrate such crimes. This has helped researchers classify the type of perpetrator being sought. This book offers readers insights into the psychology of cyber criminals, and understanding and analysing their motives and the methodologies they adopt. With an understanding of these motives, researchers, governments and practitioners can take effective measures to tackle cybercrime and reduce victimization.
Publisher: Springer
ISBN: 3319971816
Category : Social Science
Languages : en
Pages : 353
Book Description
This book provides a comprehensive overview of the current and emerging challenges of cyber criminology, victimization and profiling. It is a compilation of the outcomes of the collaboration between researchers and practitioners in the cyber criminology field, IT law and security field. As Governments, corporations, security firms, and individuals look to tomorrow’s cyber security challenges, this book provides a reference point for experts and forward-thinking analysts at a time when the debate over how we plan for the cyber-security of the future has become a major concern. Many criminological perspectives define crime in terms of social, cultural and material characteristics, and view crimes as taking place at a specific geographic location. This definition has allowed crime to be characterised, and crime prevention, mapping and measurement methods to be tailored to specific target audiences. However, this characterisation cannot be carried over to cybercrime, because the environment in which such crime is committed cannot be pinpointed to a geographical location, or distinctive social or cultural groups. Due to the rapid changes in technology, cyber criminals’ behaviour has become dynamic, making it necessary to reclassify the typology being currently used. Essentially, cyber criminals’ behaviour is evolving over time as they learn from their actions and others’ experiences, and enhance their skills. The offender signature, which is a repetitive ritualistic behaviour that offenders often display at the crime scene, provides law enforcement agencies an appropriate profiling tool and offers investigators the opportunity to understand the motivations that perpetrate such crimes. This has helped researchers classify the type of perpetrator being sought. This book offers readers insights into the psychology of cyber criminals, and understanding and analysing their motives and the methodologies they adopt. With an understanding of these motives, researchers, governments and practitioners can take effective measures to tackle cybercrime and reduce victimization.
Advances in Visual Informatics
Author: Halimah Badioze Zaman
Publisher: Springer
ISBN: 3319259393
Category : Computers
Languages : en
Pages : 535
Book Description
This book constitutes the refereed proceedings of the Fourth International Conference on Advances in Visual Informatics, IVIC 2015, held in Bangi, Malaysia, in November 2015. The five keynotes and 45 papers presented were carefully reviewed and selected from 82 initial submissions. The papers are organized in four tracks on visualization and big data; machine learning and computer vision; computer graphics; as well as virtual reality.
Publisher: Springer
ISBN: 3319259393
Category : Computers
Languages : en
Pages : 535
Book Description
This book constitutes the refereed proceedings of the Fourth International Conference on Advances in Visual Informatics, IVIC 2015, held in Bangi, Malaysia, in November 2015. The five keynotes and 45 papers presented were carefully reviewed and selected from 82 initial submissions. The papers are organized in four tracks on visualization and big data; machine learning and computer vision; computer graphics; as well as virtual reality.
Biomedical Data Management and Graph Online Querying
Author: Fusheng Wang
Publisher: Springer
ISBN: 331941576X
Category : Computers
Languages : en
Pages : 206
Book Description
This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data Management and Analytics for Medicine and Healthcare, DMAH 2015, held at Waikoloa, Hawaii, USA on August 31 and September 4, 2015, in conjunction with the 41st International Conference on Very Large Data Bases, VLDB 2015. The 9 revised full papers presented together with 5 invited papers and 1 extended abstract were carefully reviewed and selected from 22 initial submissions. The papers are organized in topical sections on information retrieval and data analytics for electronic medical records; data management and visualization of medical data; biomedical data sharing and integration; medical imaging analytics; and big-graphs online querying.
Publisher: Springer
ISBN: 331941576X
Category : Computers
Languages : en
Pages : 206
Book Description
This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data Management and Analytics for Medicine and Healthcare, DMAH 2015, held at Waikoloa, Hawaii, USA on August 31 and September 4, 2015, in conjunction with the 41st International Conference on Very Large Data Bases, VLDB 2015. The 9 revised full papers presented together with 5 invited papers and 1 extended abstract were carefully reviewed and selected from 22 initial submissions. The papers are organized in topical sections on information retrieval and data analytics for electronic medical records; data management and visualization of medical data; biomedical data sharing and integration; medical imaging analytics; and big-graphs online querying.
Guide to High Performance Distributed Computing
Author: K.G. Srinivasa
Publisher: Springer
ISBN: 3319134973
Category : Computers
Languages : en
Pages : 310
Book Description
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Features: describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing; presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution; Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding; Provides detailed case studies on approaches to clustering, data classification and regression analysis; Explains the process of creating a working recommender system using Scalding and Spark.
Publisher: Springer
ISBN: 3319134973
Category : Computers
Languages : en
Pages : 310
Book Description
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Features: describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing; presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution; Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding; Provides detailed case studies on approaches to clustering, data classification and regression analysis; Explains the process of creating a working recommender system using Scalding and Spark.
Big Data Optimization: Recent Developments and Challenges
Author: Ali Emrouznejad
Publisher: Springer
ISBN: 3319302655
Category : Technology & Engineering
Languages : en
Pages : 492
Book Description
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Publisher: Springer
ISBN: 3319302655
Category : Technology & Engineering
Languages : en
Pages : 492
Book Description
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.