Knowledge Mining
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Knowledge Mining
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Author : Spiros Sirmakessis
language : en
Publisher: Springer
Release Date : 2006-06-10
Knowledge Mining written by Spiros Sirmakessis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-10 with Computers categories.
Text mining is an exciting application ?eld and an area of scienti?c - search that is currently under rapid development. It uses techniques from well-established scienti?c ?elds (e. g. data mining, machine learning, infor- tion retrieval, natural language processing, case-based reasoning, statistics and knowledge management) in an e?ort to help people gain insight, und- stand and interpret large quantities of (usually) semi-structured and unstr- tured data. Despite the advances made during the last few years, many issues remain unresolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identi?ed, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the ?eld of Text Mining – especially in relation to IT – and whether there still remain areas to be covered. Knowledge Mining draws upon many of the key concepts of knowledge management, data mining and knowledge discovery, meta-analysis and data visualization. Within the context of scienti?c research, knowledge mining is principally concerned with the quantitative synthesis and visualization of - search results and ?ndings. The results of knowledge mining are increased scienti?c understanding along with improvements in research quality and value. Knowledge mining products can be used to highlight research opportunities, assist with the p- sentation of “best” scienti?c evidence, facilitate research portfolio mana- ment, as well as, facilitate policy setting and decision making.
Knowledge Mining Using Intelligent Agents
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Author : Satchidananda Dehuri
language : en
Publisher: World Scientific
Release Date : 2011
Knowledge Mining Using Intelligent Agents written by Satchidananda Dehuri and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Business & Economics categories.
Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.). By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data.
Advances Of Machine Learning For Knowledge Mining In Electronic Health Records
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Author : P. Mohamed Fathimal
language : en
Publisher: CRC Press
Release Date : 2025-03-11
Advances Of Machine Learning For Knowledge Mining In Electronic Health Records written by P. Mohamed Fathimal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-11 with Computers categories.
The book explores the application of cutting-edge machine learning and deep learning algorithms in mining Electronic Health Records (EHR). With the aim of improving patient health management, this book explains the structure of EHR consisting of demographics, medical history, and diagnosis, with a focus on the design and representation of structured, semi-structured, and unstructured data. Explains the design of organized, semi-structured, unstructured, and irregular time series data of electronic health records Covers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured data Discusses supervised and unsupervised learning in electronic health records Describes clustering and classification techniques for organized, semi- structured, and unstructured data from electronic health records This book is an essential resource for researchers and professionals in fields like computer science, biomedical engineering, and information technology, seeking to enhance healthcare efficiency, security, and privacy through advanced data analytics and machine learning.
Data Mining
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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05
Data Mining written by Krzysztof J. Cios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-05 with Computers categories.
“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.
Knowledge Synthesis
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Author : Yoshiteru Nakamori
language : en
Publisher: Springer
Release Date : 2015-11-26
Knowledge Synthesis written by Yoshiteru Nakamori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-26 with Political Science categories.
This book provides readers the idea of systemically synthesizing various kind of knowledge, which needs to combine analytical thinking and synthetic thinking. Systems science is expected to help in solving contemporary complex problems, utilizing interdisciplinary knowledge effectively and combining analytical thinking and synthetic thinking efficiently. However, traditional systems science has been divided into two schools: one seeks a systematic procedure to give a correct objective answer; the other develops an emergent, systemic process so that the user can continue exploratory learning. It is not an exaggeration to say that analytical thinking and synthetic thinking have been developed independently, in different schools. This book integrates approaches developed in these two schools, using ideas in knowledge science that have been emerging recently under the influence of Eastern thinking. It emphasizes the importance of utilizing intuition in systems approaches, whereas other books usually try to solve problems rationally and objectively, rejecting subjectivity. This book never denies rationality and objectivity; however, complex problems of today do not always yield to complete analysis. The novelty of this present volume is that it takes in the ideas of synthetic thinking in knowledge science to develop systems science further. The chapter contributors, who are experienced systems scientists with a profound understanding of knowledge management, discuss knowledge synthesis from the Western and Eastern cultural perspectives. The book introduces a theory on systemic knowledge synthesis in an odd chapter and then presents an application of the theory in the next chapter in order to contribute to developing translational systems science.
Data Mining And Knowledge Discovery With Evolutionary Algorithms
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Author : Alex A. Freitas
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Data Mining And Knowledge Discovery With Evolutionary Algorithms written by Alex A. Freitas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Computers categories.
This book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research. In essence, data mining consists of extracting valid, comprehensible, and in teresting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog nition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. In a nutshell, 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 (rules or another form of knowl edge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.
Feature Selection For Knowledge Discovery And Data Mining
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Author : Huan Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Feature Selection For Knowledge Discovery And Data Mining written by Huan Liu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.
Knowledge Based Information Retrieval And Filtering From The Web
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Author : Witold Abramowicz
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Knowledge Based Information Retrieval And Filtering From The Web written by Witold Abramowicz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Computers categories.
Knowledge-Based Information Retrieval and Filtering from the Web contains fifteen chapters, contributed by leading international researchers, addressing the matter of information retrieval, filtering and management of the information on the Internet. The research presented deals with the need to find proper solutions for the description of the information found on the Internet, the description of the information consumers need, the algorithms for retrieving documents (and indirectly, the information embedded in them), and the presentation of the information found. The chapters include: -Ontological representation of knowledge on the WWW; -Information extraction; -Information retrieval and administration of distributed documents; -Hard and soft modeling based knowledge capture; -Summarization of texts found on the WWW; -User profiles and personalization for web-based information retrieval system; -Information retrieval under constricted bandwidth; -Multilingual WWW; -Generic hierarchical classification using the single-link clustering; -Clustering of documents on the basis of text fuzzy similarity; -Intelligent agents for document categorization and adaptive filtering; -Multimedia retrieval and data mining for E-commerce and E-business; -A Web-based approach to competitive intelligence; -Learning ontologies for domain-specific information retrieval; -An open, decentralized architecture for searching for, and publishing information in distributed systems.
Advances In Knowledge Discovery And Data Mining
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Author : Wee Keong Ng
language : en
Publisher: Springer
Release Date : 2006-03-10
Advances In Knowledge Discovery And Data Mining written by Wee Keong Ng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-10 with Computers categories.
This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.
Information Technology
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Author : AJOY KUMAR RAY
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2004-01-01
Information Technology written by AJOY KUMAR RAY and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with Computers categories.
This comprehensive yet accessible text provides a good introduction to the fundamental concepts of Information Technology and skillfully elaborates on their applications, covering in the process the entire spectrum of IT related topics. Organized into three parts, the book offers an insightful analysis of the subject, explaining the concepts through suitable illustrations. Part I covers basic issues and concepts of Internet and the techniques of acquiring, storing, structuring and managing information that may involve images, text files and video data. The reader is exposed to both centralized and distributed database systems. Part II deals with the core topics in developing information systems which are based on audio and speech compression, multimedia communication techniques, and soft computing for analysis and interpretation of data. Part III focusses on a number of application areas-as remote sensing, telemedicine, e-commerce, cybermediary and rural development-besides the traditional engineering disciplines, highlighting their social impacts. The book is intended for undergraduate and postgraduate students of information technology, computer science as well as electronics and electrical communication engineering. It should also serve as an excellent reference for professionals in the IT field. Key Features: Discusses in detail the theoretical basis behind a web graph. Deals with security issues of computer networks and their implications in an easy-to-understand manner. Contains more than 30 projects (with useful hints) that students of various IT courses would find interesting to work on. Three chapters are exclusively devoted to different aspects of database management and data mining systems.