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Advances In Data Mining And Modeling


Advances In Data Mining And Modeling
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Advances In Data Mining And Modeling


Advances In Data Mining And Modeling
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Author : Wai Ki Ching
language : en
Publisher: World Scientific
Release Date : 2003-04-03

Advances In Data Mining And Modeling written by Wai Ki Ching and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-04-03 with Computers categories.


Data mining and data modeling are hot topics and are under fast development. Because of their wide applications and rich research contents, many practitioners and academics are attracted to work in these areas. With a view to promoting communication and collaboration among the practitioners and researchers in Hong Kong, a workshop on data mining and modeling was held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical Research, The University of Hong Kong, and Prof Tze Leung Lai (Stanford University), C V Starr Professor of the University of Hong Kong, initiated the workshop.This book contains selected papers presented at the workshop. The papers fall into two main categories: data mining and data modeling. Data mining papers deal with pattern discovery, clustering algorithms, classification and practical applications in the stock market. Data modeling papers treat neural network models, time series models, statistical models and practical applications.



Advances In Data Mining


Advances In Data Mining
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Author : Petra Perner
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-08-21

Advances In Data Mining written by Petra Perner 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 2002-08-21 with Business & Economics categories.


This book presents six thoroughly reviewed and revised full papers describing selected projects on data mining. Three papers deal with data mining and e-commerce, focusing on sequence rule analysis, association rule mining and knowledge discovery in databases, and intelligent e-marketing with Web mining. One paper is devoted to experience management and process learning. The last two papers report on medical applications, namely on genomic data processing and on case-based reasoning for prognosis of influenza.



Advanced Data Mining Techniques


Advanced Data Mining Techniques
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Author : David L. Olson
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-01

Advanced Data Mining Techniques written by David L. Olson 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 2008-01-01 with Business & Economics categories.


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.



Advances In Knowledge Discovery And Data Mining


Advances In Knowledge Discovery And Data Mining
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Author : Tru Cao
language : en
Publisher: Springer
Release Date : 2015-04-16

Advances In Knowledge Discovery And Data Mining written by Tru Cao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-16 with Computers categories.


This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015. The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations.



Research Advances In Data Mining Techniques And Applications


Research Advances In Data Mining Techniques And Applications
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2024-05-02

Research Advances In Data Mining Techniques And Applications written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Computers categories.


For contemporary societies, data mining has emerged as a serious challenge. Thanks to more advanced analytical tools, the Big Data explosion has enabled businesses to assess their performance more thoroughly and accurately. For example, transitioning from using a basic spreadsheet to using data lake modeling offers more flexibility in terms of consulting and summarizing vast amounts of data from many business angles. Data mining, which is the foundation for this optimization of data analysis, has been strengthened by artificial intelligence and machine learning to find patterns in this deluge of data and build future prediction models, turning it into a critical tool for decision-making. This book provides an understanding of the most modern techniques and uses for data mining. It examines data mining in order to classify datasets, predict outcomes, and optimize analyses. Furthermore, the book demonstrates these technological developments by highlighting relevant applications of data mining in industry, biology, education, medicine, and health.



Advances In Knowledge Discovery And Data Mining


Advances In Knowledge Discovery And Data Mining
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Author : Usama M. Fayyad
language : en
Publisher:
Release Date : 1996

Advances In Knowledge Discovery And Data Mining written by Usama M. Fayyad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.


Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.



Advanced Data Mining And Applications


Advanced Data Mining And Applications
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Author : Gao Cong
language : en
Publisher: Springer
Release Date : 2017-10-30

Advanced Data Mining And Applications written by Gao Cong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-30 with Computers categories.


This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.



Recent Advances In Data Mining Of Enterprise Data


Recent Advances In Data Mining Of Enterprise Data
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Author : T. Warren Liao
language : en
Publisher: World Scientific
Release Date : 2008-01-15

Recent Advances In Data Mining Of Enterprise Data written by T. Warren Liao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-15 with Business & Economics categories.


The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as OC enterprise dataOCO. The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making. Sample Chapter(s). Foreword (37 KB). Chapter 1: Enterprise Data Mining: A Review and Research Directions (655 KB). Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers. Readership: Graduate students in engineering, computer science, and business schools; researchers and practioners of data mining with emphazis of enterprise data mining."



Advances In Knowledge Discovery And Data Mining Part I


Advances In Knowledge Discovery And Data Mining Part I
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Author : Pang-Ning Tan
language : en
Publisher: Springer
Release Date : 2012-05-10

Advances In Knowledge Discovery And Data Mining Part I written by Pang-Ning Tan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-10 with Computers categories.


The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning: active, ensemble, rare-class and online; unsupervised learning: clustering, probabilistic modeling in the first volume and on pattern mining: networks, graphs, time-series and outlier detection, and data manipulation: pre-processing and dimension reduction in the second volume.



Advances In Predictive Models For Data Mining


Advances In Predictive Models For Data Mining
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Author : International Business Machines Corporation. Research Division. (IBMRD)
language : en
Publisher:
Release Date : 1999

Advances In Predictive Models For Data Mining written by International Business Machines Corporation. Research Division. (IBMRD) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Data mining categories.


Abstract: "Expanding application demand for data mining of massive data warehouses has fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation. Relatively new techniques for selecting good feature variables, feature discretization, generating probabilistic models, and the use of practical measures for performance will also be discussed."