Articles liés à Data Warehousing and Data Mining
Synopsis
Divided in two sections, this book is organized in 15 chapters. The first section covers data warehousing concepts and the steps required in creating a data warehouse for a decision support system along with data warehouse implementation case study. The second section provides a comprehensive introduction to data mining and is designed to be accessible and useful to students, instructors, researchers and professionals. It includes data preprocessing, visualization, predictive modeling, association analysis, clustering, and anomaly detection. The goal is to present fundamental concepts and algorithms for each topic, thus providing reader with the necessary background for the application of data mining to the real problems.
Salient Features
- Important concepts of data warehousing and data mining.
- Solved numerical problems and case studies. The exercises have been provided at the end of every chapter.
- Chapters are organized into the following sections: Objectives, Theory and examples, Summary and Solved Problems.
- Summary at the end of the chapter
- Long and short answer type questions at the end of each chapter
- Tables and figures for better illustration
- Solutions to numerical problems.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'auteur
Deepali Kamthania has more than 19 years of experience in academics and IT industry. Presently she is Professor, School of Information Technology, VIPS, Delhi. She has received B.Sc. (Hons.) and MCA degrees from Aligarh Muslim University (AMU), Aligarh and Ph.D. from Indian Institute of Technology (IIT), Delhi. Her areas of interest include machine learning and hybrid photovoltaic systems. She has published over 90 research papers in reputed national and international conferences and journals including SCI, Web of Science and Scopus along with various magazine articles and two books. She has attended and organized many workshops, guest lectures, and seminars. She has delivered many technical talks and chaired sessions at various academic forums. She serves as editorial board member/reviewer of various refereed journals. She is guiding Ph.D. scholars and is actively involved in various research projects. She has been actively involved in designing course curriculum for BCA and MCA. She has received Merit scholarship in B.Sc. and Bharat Seva Scholarship in MCA. She is the recipient of Academic Excellence Awards, Best Paper Award in 2021, 2020 and 2019, Best Teacher Award in 2018 and 2019, Active Participation Award (Woman) in 2016-17 from Computer Society of India and Institution of Engineering Technology Awards in 2013 and 2014 for significant contribution to the IT field. She is a life time member of IEEE, CSI and ISTE.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Data Warehousing And Data Mining
Vendeur : Books in my Basket, New Delhi, Inde
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N.A. Etat : New. ISBN:9789390620609. N° de réf. du vendeur 2253349
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Data Warehousing and Data Mining
Vendeur : Vedams eBooks (P) Ltd, New Delhi, Inde
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Soft cover. Etat : New. Contents: 1. Evolution of Decision Support Systems and Data Warehousing. 2. From Data to Information. 3. Data Warehouse Architecture and OLAP Servers. 4. Defining the Business Requirements. 5. Data Warehouse Environment. 6. Data Warehouse Design. 7. Data Warehouse Schema. 8. Case Studies. 9. Introduction to Data Mining. 10. Understanding Data and Data Preprocessing. 11. Frequent Pattern Mining. 12. Classification. 13. Clustering. 14. A Brief Overview of Outlier Detection Techniques. 15. Introduction to Web, Temporal and Spatial Mining. Divided in two sections, this book is organized in 15 chapters. The first section covers data warehousing concepts and the steps required in creating a data warehouse for a decision support system along with data warehouse implementation case study. The second section provides a comprehensive introduction to data mining and is designed to be accessible and useful to students, instructors, researchers and professionals. It includes data preprocessing, visualization, predictive modeling, association analysis, clustering, and anomaly detection. The goal is to present fundamental concepts and algorithms for each topic, thus providing reader with the necessary background for the application of data mining to the real problems. N° de réf. du vendeur 137911A
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