KDD2019
Author :
Publisher :
Page : pages
File Size : 20,36 MB
Release : 2019
Category : Data mining
ISBN : 9781450362016
Author :
Publisher :
Page : pages
File Size : 20,36 MB
Release : 2019
Category : Data mining
ISBN : 9781450362016
Author : Inderjit S. Dhillon
Publisher :
Page : 1534 pages
File Size : 50,19 MB
Release : 2013
Category : Computer science
ISBN : 9781450321747
Author : Oded Maimon
Publisher : Springer Science & Business Media
Page : 1378 pages
File Size : 41,60 MB
Release : 2006-05-28
Category : Computers
ISBN : 038725465X
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.
Author : A. Zanasi
Publisher : WIT Press (UK)
Page : 1042 pages
File Size : 18,20 MB
Release : 2002
Category : Computers
ISBN :
Data mining brings together techniques from machine learning, pattern recognition, statistics, databases, linguistics and visualization in order to extract information from large databases. Originally principally concerned with behavioural applications, such as the understanding of customer behaviour, its scope has now been widened with the introduction of Text Mining techniques. Areas now encompassed by data mining include military, market, and competitive intelligence applications, taxonomies and internet search techniques, and knowledge management applications.
Author : Robert Grossman
Publisher :
Page : pages
File Size : 19,91 MB
Release : 2013-08-11
Category :
ISBN : 9781450325721
KDD'13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Aug 11, 2013-Aug 14, 2013 Chicago, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Author : Tsau Young Lin
Publisher : Springer Science & Business Media
Page : 562 pages
File Size : 42,31 MB
Release : 2008-08-20
Category : Mathematics
ISBN : 354078487X
The IEEE ICDM 2004 workshop on the Foundation of Data Mining and the IEEE ICDM 2005 workshop on the Foundation of Semantic Oriented Data and Web Mining focused on topics ranging from the foundations of data mining to new data mining paradigms. The workshops brought together both data mining researchers and practitioners to discuss these two topics while seeking solutions to long standing data mining problems and stimul- ing new data mining research directions. We feel that the papers presented at these workshops may encourage the study of data mining as a scienti?c ?eld and spark new communications and collaborations between researchers and practitioners. Toexpressthevisionsforgedintheworkshopstoawiderangeofdatam- ing researchers and practitioners and foster active participation in the study of foundations of data mining, we edited this volume by involving extended and updated versions of selected papers presented at those workshops as well as some other relevant contributions. The content of this book includes st- ies of foundations of data mining from theoretical, practical, algorithmical, and managerial perspectives. The following is a brief summary of the papers contained in this book.
Author : Ramon Lopez de Mantaras
Publisher : Springer Science & Business Media
Page : 469 pages
File Size : 22,62 MB
Release : 2000-05-17
Category : Computers
ISBN : 3540676023
This book constitutes the refereed proceedings of the 11th European Conference on Machine Learning, ECML 2000, held in Barcelona, Catalonia, Spain, in May/June 2000. The 20 long papers and 23 short papers presented together with 2 invited contributions were carefully reviewed and selected from 100 submissions. All current issues in machine learning as well as advanced applications in various areas are addressed.
Author : Jan Zytkow
Publisher : Springer Science & Business Media
Page : 608 pages
File Size : 25,90 MB
Release : 1999-09-01
Category : Computers
ISBN : 3540664904
This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.
Author : Oded Maimon
Publisher : World Scientific Publishing Company
Page : 344 pages
File Size : 50,70 MB
Release : 2005-05-30
Category : Computers
ISBN : 9813106441
Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery in Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools. The various elements are then joined together to solve the initial problem. The benefits of decomposition methodology in data mining include: increased performance (classification accuracy); conceptual simplification of the problem; enhanced feasibility for huge databases; clearer and more comprehensible results; reduced runtime by solving smaller problems and by using parallel/distributed computation; and the opportunity of using different techniques for individual sub-problems.
Author : Richard Ellis
Publisher : Springer Science & Business Media
Page : 504 pages
File Size : 32,36 MB
Release : 2009-10-28
Category : Computers
ISBN : 1848829833
The most common document formalisation for text classi?cation is the vector space model founded on the bag of words/phrases representation. The main advantage of the vector space model is that it can readily be employed by classi?cation - gorithms. However, the bag of words/phrases representation is suited to capturing only word/phrase frequency; structural and semantic information is ignored. It has been established that structural information plays an important role in classi?cation accuracy [14]. An alternative to the bag of words/phrases representation is a graph based rep- sentation, which intuitively possesses much more expressive power. However, this representation introduces an additional level of complexity in that the calculation of the similarity between two graphs is signi?cantly more computationally expensive than between two vectors (see for example [16]). Some work (see for example [12]) has been done on hybrid representations to capture both structural elements (- ing the graph model) and signi?cant features using the vector model. However the computational resources required to process this hybrid model are still extensive.