Heuristic And Optimization For Knowledge Discovery
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Heuristic And Optimization For Knowledge Discovery
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Author :
language : en
Publisher:
Release Date : 2002
Heuristic And Optimization For Knowledge Discovery written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Heuristic And Optimization For Knowledge Discovery
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Author : Abbass, Hussein A.
language : en
Publisher: IGI Global
Release Date : 2001-07-01
Heuristic And Optimization For Knowledge Discovery written by Abbass, Hussein A. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-01 with Computers categories.
With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.
Heuristic And Optimization For Knowledge Discovery
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Author : Ruhul A. Sarker
language : en
Publisher:
Release Date : 2002
Heuristic And Optimization For Knowledge Discovery written by Ruhul A. Sarker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Combinatorial optimization categories.
Metaheuristics For Big Data
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Author : Clarisse Dhaenens
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-16
Metaheuristics For Big Data written by Clarisse Dhaenens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-16 with Computers categories.
Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Data Mining A Heuristic Approach
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Author : Abbass, Hussein A.
language : en
Publisher: IGI Global
Release Date : 2001-07-01
Data Mining A Heuristic Approach written by Abbass, Hussein A. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-01 with Computers categories.
Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.
Knowledge Discovery In Databases
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Author : Gregory Piatetsky-Shapiro
language : en
Publisher: MIT Press
Release Date : 1991
Knowledge Discovery In Databases written by Gregory Piatetsky-Shapiro and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called discovery in databases. This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search.Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTE Laboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.
Soft Computing For Knowledge Discovery And Data Mining
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Author : Oded Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-25
Soft Computing For Knowledge Discovery And Data Mining written by Oded Maimon 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 2007-10-25 with Computers categories.
Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.
Feature Selection For Knowledge Discovery And Data Mining
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Author : Huan Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 1998-07-31
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 1998-07-31 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.
Advances In Knowledge Discovery And Management
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Author : Fabrice Guillet
language : en
Publisher: Springer
Release Date : 2016-11-03
Advances In Knowledge Discovery And Management written by Fabrice Guillet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-03 with Computers categories.
This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.
Data Driven Optimization And Knowledge Discovery For An Enterprise Information System
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Author : Qing Duan
language : en
Publisher: Springer
Release Date : 2015-06-13
Data Driven Optimization And Knowledge Discovery For An Enterprise Information System written by Qing Duan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-13 with Technology & Engineering categories.
This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.