Download or read book Advancing Knowledge Discovery and Data Mining Technologies written by Qihai Zhou and published by . This book was released on 2009 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advanced Techniques in Knowledge Discovery and Data Mining written by Nikhil Pal and published by Springer. This book was released on 2005-07-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.
Download or read book Advanced Techniques in Knowledge Discovery and Data Mining written by Nikhil Pal and published by Springer Science & Business Media. This book was released on 2007-12-31 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.
Download or read book Advanced Data Mining Techniques written by David L. Olson and published by Springer Science & Business Media. This book was released on 2008-01-01 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.
Download or read book Data Mining and Knowledge Discovery Technologies written by Taniar, David and published by IGI Global. This book was released on 2008-01-31 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: As information technology continues to advance in massive increments, the bank of information available from personal, financial, and business electronic transactions and all other electronic documentation and data storage is growing at an exponential rate. With this wealth of information comes the opportunity and necessity to utilize this information to maintain competitive advantage and process information effectively in real-world situations. Data Mining and Knowledge Discovery Technologies presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics.
Download or read book Research and Trends in Data Mining Technologies and Applications written by Taniar, David and published by IGI Global. This book was released on 2006-10-31 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.
Download or read book Advanced Methods for Knowledge Discovery from Complex Data written by Ujjwal Maulik and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.
Download or read book Feature Selection for Knowledge Discovery and Data Mining written by Huan Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Mining a search for knowledge written by Mohamed Rahama and published by GRIN Verlag. This book was released on 2012-10-23 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essay from the year 2012 in the subject Computer Science - Theory, grade: B, ( Atlantic International University ) (School of Science and Engineering), course: Doctorate in Information Technology, language: English, abstract: Data mining is an independent science that based on advanced ways for information retrieval. Data mining is dealing with knowledge discovery in data warehouses without predefined hypotheses. So it is quite different from other applications such as decision support systems, OLAP and others which are looking for information on the factors and assumptions that we know it in advance. Data Mining supports multiple algorithms which have the ability to adopt automatic classification of historical data and predict future events. Data mining in the databases is designed to extract the hidden information, and it is a modern technology that imposed itself strongly in the information revolution, in the light of the great technological development and widespread use of data warehouses. Data mining techniques focus on building future forecasts and explore the behavior and trends, allowing a good estimation for right decisions that taken in a timely manner. This paper provides a general definition of data mining science and its most important techniques and algorithms used.
Download or read book Information Visualization in Data Mining and Knowledge Discovery written by Usama M. Fayyad and published by Morgan Kaufmann. This book was released on 2002 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Download or read book Feature Selection for Knowledge Discovery and Data Mining written by Huan Liu and published by Springer. This book was released on 2013-01-27 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Beyond Databases Architectures and Structures Advanced Technologies for Data Mining and Knowledge Discovery written by Stanisław Kozielski and published by Springer. This book was released on 2016-04-28 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2016, held in Ustroń, Poland, in May/June 2016. It consists of 57 carefully reviewed papers selected from 152 submissions. The papers are organized in topical sections, namely artificial intelligence, data mining and knowledge discovery; architectures, structures and algorithms for efficient data processing; data warehousing and OLAP; natural language processing, ontologies and semantic Web; bioinformatics and biomedical data analysis; data processing tools; novel applications of database systems.
Download or read book Advanced Methods for Knowledge Discovery from Complex Data written by Ujjwal Maulik and published by Springer. This book was released on 2005-11-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.
Download or read book Real world Data Mining written by Dursun Delen and published by Pearson Education. This book was released on 2015 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: As business becomes increasingly complex and global, decision-makers must act more rapidly and accurately, based on the best available evidence. Modern data mining and analytics is indispensable for doing this. Real-World Data Mining demystifies current best practices, showing how to use data mining and analytics to uncover hidden patterns and correlations, and leverage these to improve all business decision-making. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, Delen provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: data mining processes, methods, and techniques; the role and management of data; tools and metrics; text and web mining; sentiment analysis; and integration with cutting-edge Big Data approaches. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials.
Download or read book Intelligent Technologies for Information Analysis written by Ning Zhong and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 book Intelligent Soft Computation and Evolving Data Mining Integrating Advanced Technologies written by Wang, Leon Shyue-Liang and published by IGI Global. This book was released on 2010-03-31 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a reference to researchers, practitioners, and students in both soft computing and data mining communities for generating creative ideas of securing and managing data mining"--Provided by publisher.
Download or read book Machine Learning and Knowledge Discovery for Engineering Systems Health Management written by Ashok N. Srivastava and published by CRC Press. This book was released on 2016-04-19 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.
